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Memetic Algorithms

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Handbook of Heuristics

Abstract

Memetic algorithms provide one of the most effective and flexible metaheuristic approaches for tackling hard optimization problems. Memetic algorithms address the difficulty of developing high-performance universal heuristics by encouraging the exploitation of multiple heuristics acting in concert, making use of all available sources of information for a problem. This approach has resulted in a rich arsenal of heuristic algorithms and metaheuristic frameworks for many problems. This chapter discusses the philosophy of the memetic paradigm, lays out the structure of a memetic algorithm, develops several example algorithms, surveys recent work in the field, and discusses the possible future directions of memetic algorithms.

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References

  1. Abbass HA (2002) An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif Intell Med 25(3):265–281

    Article  Google Scholar 

  2. Afsar HM, Prins C, Santos AC (2014) Exact and heuristic algorithms for solving the generalized vehicle routing problem with flexible fleet size. Int Trans Oper Res 21(1):153–175

    Article  MathSciNet  MATH  Google Scholar 

  3. Ahammed F, Moscato P (2011) Evolving L-systems as an intelligent design approach to find classes of difficult-to-solve traveling salesman problem instances. In: Di Chio C et al (eds) Applications of Evolutionary Computation. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, pp 1–11

    Chapter  Google Scholar 

  4. Ahandani MA, Vakil-Baghmisheh MT, Talebi M (2014) Hybridizing local search algorithms for global optimization. Comput Optim Appl 59(3):725–748

    Article  MathSciNet  MATH  Google Scholar 

  5. Ahn Y, Park J, Lee CG, Kim JW, Jung SY (2010) Novel memetic algorithm implemented with GA (genetic algorithm) and MADS (mesh adaptive direct search) for optimal design of electromagnetic system. IEEE Trans Magn 46(6):1982–1985

    Article  Google Scholar 

  6. Al-Betar MA, Khader AT, Abu Doush I (2014) Memetic techniques for examination timetabling. Ann Oper Res 218(1):23–50

    Article  MathSciNet  MATH  Google Scholar 

  7. Alba E (2005) Parallel metaheuristics: a new class of algorithms. Wiley-Interscience, Hoboken

    Book  MATH  Google Scholar 

  8. Aldous D, Vazirani U (1994) “go with the winners” algorithms. In: Proceedings of 35th IEEE Symposium on Foundations of Computer Science. IEEE Press, Los Alamitos, pp 492–501

    Chapter  Google Scholar 

  9. Ali AF, Hassanien AE, Snasel V, Tolba MF (2014) A new hybrid particle swarm optimization with variable neighborhood search for solving unconstrained global optimization problems. In: Kromer P, Abraham A, Snasel V (eds) Fifth International Conference on Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 303. Springer, Berlin/Ostrava, pp 151–160

    Google Scholar 

  10. Amaya JE, Cotta C, Fernández-Leiva AJ (2012) Solving the tool switching problem with memetic algorithms. Artif Intell Eng Des Anal Manuf 26:221–235

    Article  Google Scholar 

  11. Amaya JE, Cotta C, Fernández-Leiva AJ (2013) Cross entropy-based memetic algorithms: an application study over the tool switching problem. Int J Comput Intell Syst 6(3):559–584

    Article  Google Scholar 

  12. Andres Gallo C, Andrea Carballido J, Ponzoni I (2009) BiHEA: a hybrid evolutionary approach for microarray biclustering. In: Guimaraes K, Panchenko A, Przytycka T (eds) 4th Brazilian Symposium on Bioinformatics (BSB 2009). Lecture Notes in Bioinformatics, vol 5676. Springer, Berlin/Porto Alegre, pp 36–47

    Google Scholar 

  13. Andres Gallo C, Andrea Carballido J, Ponzoni I (2009) Microarray biclustering: a novel memetic approach based on the PISA platform. In: Pizzuti C, Ritchie M, Giacobini M (eds) 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Lecture Notes in Computer Science, vol 5483. Springer, Berlin/Tubingen, pp 44–55

    Google Scholar 

  14. António CC (2014) A memetic algorithm based on multiple learning procedures for global optimal design of composite structures. Memetic Comput 6(2):113–131

    Article  Google Scholar 

  15. Arab A, Alfi A (2015) An adaptive gradient descent-based local search in memetic algorithm applied to optimal controller design. Inf Sci 299:117–142

    Article  MathSciNet  Google Scholar 

  16. Arivudainambi D, Balaji S, Rekha D (2014) Improved memetic algorithm for energy efficient target coverage in wireless sensor networks. In: 11th IEEE International Conference on Networking, Sensing and Control (ICNSC), Miami, pp 261–266

    Google Scholar 

  17. Arshi SS, Zolfaghari A, Mirvakili SM (2014) A multi-objective shuffled frog leaping algorithm for in-core fuel management optimization. Comput Phys Commun 185(10):2622–2628

    Article  Google Scholar 

  18. de Assis LS, Vizcaino Gonzalez JF, Usberti FL, Lyra C, Cavellucci C, Von Zuben FJ (2015) Switch allocation problems in power distribution systems. IEEE Trans Power Syst 30(1):246–253

    Article  Google Scholar 

  19. Ayadi W, Hao JK (2014) A memetic algorithm for discovering negative correlation biclusters of DNA microarray data. Neurocomputing 145:14–22

    Article  Google Scholar 

  20. Aziz M, Tayarani-N MH (2014) An adaptive memetic particle swarm optimization algorithm for finding large-scale latin hypercube designs. Eng Appl Artif Intell 36:222–237

    Article  Google Scholar 

  21. Baghmisheh MTV, Ahandani MA, Talebi M (2008) Frequency modulation sound parameter identification using novel hybrid evolutionary algorithms. In: International Symposium on Telecommunications. IEEE, Tehran, pp 67–72

    Google Scholar 

  22. Benlic U, Hao JK (2015) Memetic search for the quadratic assignment problem. Expert Syst Appl 42(1):584–595

    Article  Google Scholar 

  23. Berns A, Ghosh S (2009) Dissecting self-⋆ properties. In: Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems. IEEE Press, San Francisco, pp 10–19

    Chapter  Google Scholar 

  24. Berretta R, Cotta C, Moscato P (2012) Memetic algorithms in bioinformatics. In: Neri F, Cotta C, Moscato P (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 261–271

    Chapter  Google Scholar 

  25. Bertagnoli G, Giordano L, Mancini S (2014) Optimization of concrete shells using genetic algorithms. ZAMM: Zeitschrift für Angewandte Mathematik und Mechanik 94(1–2, SI):43–54

    Google Scholar 

  26. Biao S, Hua HC, Hua YX, Chuan H (2014) Mutation particle swarm optimization algorithm for solving the optimal operation model of thermal power plants. J Renew Sustain Energy 6(4):043118

    Article  Google Scholar 

  27. Bilal N, Galinier P, Guibault F (2014) An iterated-tabu-search heuristic for a variant of the partial set covering problem. J Heuristics 20(2):143–164

    Article  Google Scholar 

  28. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308

    Article  Google Scholar 

  29. Blum C, Blesa Aguilera MJ, Roli A, Sampels M (2008) Hybrid metaheuristics: an emerging approach to optimization. Studies in computational intelligence, vol 144. Springer, Berlin/Heidelberg

    Google Scholar 

  30. Blum C, Cotta C, Fernández AJ, Gallardo JE, Mastrolilli M (2008) Hybridizations of metaheuristics with branch & bound derivates. In: Blum C, Blesa Aguilera MJ, Roli A, Sampels M (eds) Hybrid Metaheuristics, an Emerging Approach to Optimization. Studies in Computational Intelligence, vol 144. Springer, Berlin/Heidelberg, pp 85–116

    Google Scholar 

  31. Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11(6):4135–4151

    Article  MATH  Google Scholar 

  32. Bose D, Biswas S, Vasilakos AV, Laha S (2014) Optimal filter design using an improved artificial bee colony algorithm. Inform Sci 281:443–461

    Article  MathSciNet  Google Scholar 

  33. Boskovic B, Brglez F, Brest J (2014) Low-autocorrelation binary sequences: on the performance of memetic-tabu and self-avoiding walk solvers. CoRR abs/1406.5301

    Google Scholar 

  34. Bullinaria JA, AlYahya K (2014) Artificial bee colony training of neural networks: comparison with back-propagation. Memetic Comput 6(3):171–182

    Article  Google Scholar 

  35. Cai K, Zhang J, Zhou C, Cao X, Tang K (2012) Using computational intelligence for large scale air route networks design. Appl Soft Comput 12(9):2790–2800

    Article  Google Scholar 

  36. Caorsi S, Massa A, Pastorino M, Randazzo A (2003) Electromagnetic detection of dielectric scatterers using phaseless synthetic and real data and the memetic algorithm. IEEE Trans Geosci Remote Sens 41(12):2745–2753

    Article  Google Scholar 

  37. Caponio A, Neri F (2012) Memetic algorithms in engineering and design. In: Neri F, Cotta C, Moscato P (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 241–260

    Chapter  Google Scholar 

  38. Caponio A, Neri F, Tirronen V (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput 13(8–9, SI):811–831

    Google Scholar 

  39. Capp A, Inostroza-Ponta M, Bill D, Moscato P, Lai C, Christie D, Lamb D, Turner S, Joseph D, Matthews J, Atkinson C, North J, Poulsen M, Spry NA, Tai KH, Wynne C, Duchesne G, Steigler A, Denham JW (2009) Is there more than one proctitis syndrome? A revisitation using data from the TROG 96.01 trial. Radiother Oncol 90(3):400–407

    Article  Google Scholar 

  40. Cattaruzza D, Absi N, Feillet D, Vidal T (2014) A memetic algorithm for the multi trip vehicle routing problem. Eur J Oper Res 236(3):833–848

    Article  MathSciNet  MATH  Google Scholar 

  41. Chabuk T, Reggia J, Lohn J, Linden D (2012) Causally-guided evolutionary optimization and its application to antenna array design. Integr Comput Aided Eng 19(2):111–124

    Google Scholar 

  42. Chakhlevitch K, Cowling P (2008) Hyperheuristics: recent developments. In: Cotta C, Sevaux M, Sörensen K (eds) Adaptive and Multilevel Metaheuristics. Studies in Computational Intelligence, vol 136. Springer, Berlin/Heidelberg, pp 3–29

    Chapter  Google Scholar 

  43. Chen X, Ong YS (2012) A conceptual modeling of meme complexes in stochastic search. IEEE Trans Syst Man Cybern C 42(5):612–625

    Article  Google Scholar 

  44. Chen X, Ong YS, Feng L, Lim MH, Chen C, Ho CS (2013) Towards believable resource gathering behaviours in real-time strategy games with a memetic ant colony system. In: Cho S, Sato A, Kim K (eds) 17th Asia Pacific Symposium on Intelligent and Evolutionary Systems. Procedia Computer Science, vol 24. Elsevier, Seoul, pp 143–151

    Google Scholar 

  45. Chiam SC, Tan KC, Mamun AM (2009) A memetic model of evolutionary pso for computational finance applications. Expert Syst Appl 36(2):3695–3711

    Article  Google Scholar 

  46. Chowdhury A, Giri R, Ghosh A, Das S, Abraham A, Snasel V (2010) Linear antenna array synthesis using fitness-adaptive differential evolution algorithm. In: IEEE Congress on Evolutionary Computation (CEC 2010). IEEE, Barcelona

    Google Scholar 

  47. Conradie AVE, Aldrich C (2010) Neurocontrol of a multi-effect batch distillation pilot plant based on evolutionary reinforcement learning. Chem Eng Sci 65(5):1627–1643

    Article  Google Scholar 

  48. Cotta C (2003) Protein structure prediction using evolutionary algorithms hybridized with backtracking. In: Mira J, Álvarez J (eds) Artificial Neural Nets Problem Solving Methods. Lecture Notes in Computer Science, vol 2687. Springer, Berlin/Heidelberg, pp 321–328

    Chapter  Google Scholar 

  49. Cotta C (2005) Memetic algorithms with partial lamarckism for the shortest common supersequence problem. In: Mira J, Álvarez J (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Lecture Notes in Computer Science, vol 3562. Springer, Berlin/Heidelberg, pp 84–91

    Chapter  Google Scholar 

  50. Cotta C, Fernández A (2007) Memetic algorithms in planning, scheduling, and timetabling. In: Dahal K, Tan K, Cowling P (eds) Evolutionary Scheduling. Studies in Computational Intelligence, vol 49. Springer, Berlin, pp 1–30

    Chapter  Google Scholar 

  51. Cotta C, Moscato P (2003) A mixed evolutionary-statistical analysis of an algorithm’s complexity. Appl Math Lett 16:41–47

    Article  MATH  Google Scholar 

  52. Cotta C, Moscato P (2004) Evolutionary computation: challenges and duties. In: Menon A (ed) Frontiers of Evolutionary Computation. Kluwer Academic, Boston, pp 53–72

    Chapter  Google Scholar 

  53. Cotta C, Moscato P (2005) The parameterized complexity of multiparent recombination. In: Proceedings of the 6th Metaheuristic International Conference – MIC 2005, Universität Wien, Vienna, pp 237–242

    Google Scholar 

  54. Cotta C, Troya JM (2000) On the influence of the representation granularity in heuristic forma recombination. In: Carroll J, Damiani E, Haddad H, Oppenheim D (eds) Applied Computing 2000. ACM, New York, pp 433–439

    Google Scholar 

  55. Cotta C, Troya JM (2003) Embedding branch and bound within evolutionary algorithms. Appl Intell 18(2):137–153

    Article  MATH  Google Scholar 

  56. Cotta C, Dotú I, Fernández AJ, Van Hentenryck P (2006) Scheduling social golfers with memetic evolutionary programming. In: Almeida F et al (eds) Hybrid Metaheuristics – HM 2006. Lecture Notes in Computer Science, vol 4030. Springer, Berlin/Heidelberg, pp 150–161

    Google Scholar 

  57. Cotta C, Dotú I, Fernández AJ, Van Hentenryck P (2007) Local search-based hybrid algorithms for finding golomb rulers. Constraints 12(3):263–291

    Article  MathSciNet  MATH  Google Scholar 

  58. Cotta C, Sevaux M, Sörensen K (2008) Adaptive and multilevel metaheuristics. Studies in computational intelligence, vol 136. Springer, Berlin/Heidelberg

    Google Scholar 

  59. Cotta C, Fernández Leiva AJ, Gallardo JE (2012) Memetic algorithms and complete techniques. In: Neri F, Cotta C, Moscato P (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 189–200

    Chapter  Google Scholar 

  60. Cowling P, Kendall G, Soubeiga E (2008) A hyperheuristic approach to schedule a sales submit. In: Burke E, Erben W (eds) PATAT 2000. Lecture Notes in Computer Science, vol 2079. Springer, Berlin/Heidelberg, pp 176–190

    Google Scholar 

  61. Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold Computer Library, New York

    Google Scholar 

  62. Dawkins R (1976) The selfish gene. Clarendon Press, Oxford

    Google Scholar 

  63. Dechter R (1999) Bucket elimination: a unifying framework for reasoning. Artif Intell 113(1–2):41–85

    Article  MathSciNet  MATH  Google Scholar 

  64. Detcher R, Rish I (2003) Mini-buckets: a general scheme for bounded inference. J ACM 50(2):107–153

    Article  MathSciNet  MATH  Google Scholar 

  65. Devi S, Jadhav DG, Pattnaik SS (2011) PSO based memetic algorithm for unimodal and multimodal function optimization. In: Panigrahi B, Suganthan P, Das S, Satapathy S (eds) 2nd Swarm, Evolutionary and Memetic Computing Conference. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, pp 127–134

    Google Scholar 

  66. Di Gesù V, Lo Bosco G, Millonzi F, Valenti C (2008) A memetic algorithm for binary image reconstruction. In: Proceedings of the 2008 International Workshop on Combinatorial Image Analysis, Buffalo. Lecture Notes in Computer Science, vol 4958, pp 384–395

    Google Scholar 

  67. Dimitroulas DK, Georgilakis PS (2011) A new memetic algorithm approach for the price based unit commitment problem. Appl Energy 88(12):4687–4699

    Article  Google Scholar 

  68. Divsalar A, Vansteenwegen P, Sorensen K, Cattrysse D (2014) A memetic algorithm for the orienteering problem with hotel selection. Eur J Oper Res 237(1):29–49

    Article  MATH  Google Scholar 

  69. Droste S, Jansen T, Wegener I (1999) Perhaps not a free lunch but at least a free appetizer. In: Banzhaf W, Daida JM, Eiben AE, Garzon MH, Honavar V, Jakiela MJ, Smith RE (eds) Proceedings of the First Genetic and Evolutionary Computation Conference – GECCO 1999. Morgan Kaufmann, Orlando, pp 833–839

    Google Scholar 

  70. Droste S, Jansen T, Wegener I (2002) Optimization with randomized search heuristics – the (A)NFL theorem, realistic scenarios, and difficult functions. Theor Comput Sci 287(1):131–144

    Article  MathSciNet  MATH  Google Scholar 

  71. Du J, Rada R (2012) Memetic algorithms, domain knowledge, and financial investing. Memetic Comput 4(2):109–125

    Article  Google Scholar 

  72. Duan H, Yu X (2007) Hybrid ant colony optimization using memetic algorithm for traveling salesman problem. In: IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning. IEEE, Honolulu, pp 92–95

    Chapter  Google Scholar 

  73. Duval B, Hao JK (2010) Advances in metaheuristics for gene selection and classification of microarray data. Brief Bioinform 11(1):127–141

    Article  Google Scholar 

  74. Duval B, Hao JK, Hernández JCH (2009) A memetic algorithm for gene selection and molecular classification of cancer. In: 11th Annual Conference on Genetic and Evolutionary Computation – GECCO ’09. ACM, New York, pp 201–208

    Chapter  Google Scholar 

  75. Egea JA, Balsa-Canto E, Garcia MSG, Banga JR (2009) Dynamic optimization of nonlinear processes with an enhanced scatter search method. Ind Eng Chem Res 48(9):4388–4401

    Article  Google Scholar 

  76. Eiben AE, Raue PE, Ruttkay Z (1994) Genetic algorithms with multi-parent recombination. In: Davidor Y, Schwefel HP, Männer R (eds) Parallel Problem Solving from Nature III. Lecture Notes in Computer Science, vol 866. Springer, Berlin/Heidelberg, pp 78–87

    Chapter  Google Scholar 

  77. Ellabaan M, Ong YS, Handoko SD, Kwoh CK, Man HY (2013) Discovering unique, low-energy transition states using evolutionary molecular memetic computing. IEEE Comput Intell Mag 8(3):54–63

    Article  Google Scholar 

  78. Ellabaan MM, Handoko SD, Ong YS, Kwoh CK, Bahnassy SA, Elassawy FM, Man HY (2012) A tree-structured covalent-bond-driven molecular memetic algorithm for optimization of ring-deficient molecules. Comput Math Appl 64(12, SI):3792–3804

    Google Scholar 

  79. Ellabaan MMH, Chen X, Nguyen QH (2012) Multi-modal valley-adaptive memetic algorithm for efficient discovery of first-order saddle points. In: Bui L et al (eds) Simulated Evolution and Learning. Lecture Notes in Computer Science, vol 7673. Berlin/Heidelberg, pp 83–92

    Google Scholar 

  80. Ellabaan MMH, Ong YS, Nguyen QC, Kuo JL (2012) Evolutionary discovery of transition states in water clusters. J Theor Comput Chem 11(5):965–995

    Article  Google Scholar 

  81. Eremeev AV (2008) On complexity of optimal recombination for binary representations of solutions. Evol Comput 16(1):127–147

    Article  Google Scholar 

  82. Eremeev AV (2011) On complexity of the optimal recombination for the travelling salesman problem. In: Proceedings of the 11th European Conference on Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, vol 6622. Springer, Berlin, pp 215–225

    Google Scholar 

  83. Eremeev AV, Kovalenko JV (2014) Optimal recombination in genetic algorithms, Part II. Yugoslav J Oper Res 24(2):165–186

    Article  MathSciNet  MATH  Google Scholar 

  84. Fathi M, Rodriguez V, Jesus Alvarez M (2014) A novel memetic ant colony optimization-based heuristic algorithm for solving the assembly line part feeding problem. Inter J Adv Manuf Technol 75(1–4):629–643

    Article  Google Scholar 

  85. Fister I, Fister I Jr, Brest J, Zumer V (2012) Memetic artificial bee colony algorithm for large-scale global optimization. In: IEEE Congress on Evolutionary Computation (CEC 2012). IEEE, Brisbane

    Google Scholar 

  86. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York

    MATH  Google Scholar 

  87. França PM, Gupta JND, Mendes AS, Moscato P, Veltnik KJ (2005) Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups. Comput Ind Eng 48:491–506

    Article  Google Scholar 

  88. Freisleben B, Merz P (1996) A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems. In: 1996 IEEE International Conference on Evolutionary Computation, Nagoya. IEEE Press, pp 616–621

    Chapter  Google Scholar 

  89. Gallardo JE (2012) A multilevel probabilistic beam search algorithm for the shortest common supersequence problem. PLoS ONE 7(12):1–14

    Article  Google Scholar 

  90. Gallardo JE, Cotta C (2015) A GRASP-based memetic algorithm with path relinking for the far from most string problem. Eng Appl Artif Intell. doi:10.1016/j.engappai.2015.01.020

    Google Scholar 

  91. Gallardo JE, Cotta C, Fernández AJ (2006) A memetic algorithm with bucket elimination for the still life problem. In: Gottlieb J, Raidl G (eds) Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, vol 3906. Springer, Berlin/Heidelberg, pp 73–85

    Chapter  Google Scholar 

  92. Gallardo JE, Cotta C, Fernández AJ (2009) Finding low autocorrelation binary sequences with memetic algorithms. Appl Soft Comput 9(4):1252–1262

    Article  Google Scholar 

  93. Gallardo JE, Cotta C, Fernández AJ (2009) Solving weighted constraint satisfaction problems with memetic/exact hybrid algorithms. J Artif Intell Res 35:533–555

    MathSciNet  MATH  Google Scholar 

  94. Gallo CA, Carballido JA, Ponzoni I (2009) Microarray biclustering: a novel memetic approach based on the PISA platform. In: Pizzuti C, Ritchie MD, Giacobini M (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Tübingen. Lecture Notes in Computer Science, vol 5483. Berlin/Heidelberg, pp 44–55

    Google Scholar 

  95. Gao L, Zhang C, Li X, Wang L (2014) Discrete electromagnetism-like mechanism algorithm for assembly sequences planning. Int J Prod Res 52(12):3485–3503

    Article  Google Scholar 

  96. García-Sánchez P, González J, Castillo P, Arenas M, Merelo-Guervós J (2013) Service oriented evolutionary algorithms. Soft Comput 17(6):1059–1075

    Article  Google Scholar 

  97. Garcia-Valverde T, Garcia-Sola A, Botia JA, Gomez-Skarmeta A (2012) Automatic design of an indoor user location infrastructure using a memetic multiobjective approach. IEEE Trans Syst Man Cybern C Appl Rev 42(5, SI):704–709

    Google Scholar 

  98. Ghosh P, Zafar H (2010) Linear array geometry synthesis with minimum side lobe level and null control using dynamic multi-swarm particle swarm optimizer with local search. In: Panigrahi B, Das S, Suganthan P, Dash S (eds) 1st International Conference on Swarm, Evolutionary, and Memetic Computing. Lecture Notes in Computer Science, vol 6466. Springer, Berlin/Chennai, pp 701–708

    Chapter  Google Scholar 

  99. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc., Boston

    MATH  Google Scholar 

  100. Goldberg DE, Lingle R Jr (1985) Alleles, loci, and the traveling salesman problem. In: Grefenstette JJ (ed) Proceedings of the First International Conference on Genetic Algorithms and Their Applications. Lawrence Erlbaum Associates, Hillsdale

    Google Scholar 

  101. Goudos SK, Gotsis KA, Siakavara K, Vafiadis EE, Sahalos JN (2013) A multi-objective approach to subarrayed linear antenna arrays design based on memetic differential evolution. IEEE Trans Antennas and Propag 61(6):3042–3052

    Article  MathSciNet  Google Scholar 

  102. Gu X, Li Y, Jia J (2015) Feature selection for transient stability assessment based on kernelized fuzzy rough sets and memetic algorithm. Int J Electr Power Energy Syst 64:664–670

    Article  Google Scholar 

  103. Guimaraes FG, Lowther DA, Ramirez JA (2008) Analysis of the computational cost of approximation-based hybrid evolutionary algorithms in electromagnetic design. IEEE Trans Magn 44(6):1130–1133

    Article  Google Scholar 

  104. Handoko SD, Ouyang X, Su CTT, Kwoh CK, Ong YS (2012) QuickVina: accelerating AutoDock Vina using gradient-based heuristics for global optimization. IEEE-ACM Trans Comput Biol Bioinf 9(5):1266–1272

    Article  Google Scholar 

  105. Hansen P, Mladenović N (2001) Variable neighborhood search: principles and applications. Eur J Oper Res 130(3):449–467

    Article  MathSciNet  MATH  Google Scholar 

  106. Hao J (2012) Memetic algorithms in discrete optimization. In: Neri F, Cotta C, Moscato P (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 73–94

    Chapter  Google Scholar 

  107. Hart W, Belew R (1991) Optimizing an arbitrary function is hard for the genetic algorithm. In: Belew R, Booker L (eds) Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, pp 190–195

    Google Scholar 

  108. Hervas C, Silva M (2007) Memetic algorithms-based artificial multiplicative neural models selection for resolving multi-component mixtures based on dynamic responses. Chemom Intel Lab Syst 85(2):232–242

    Article  Google Scholar 

  109. Hirsch R, Mullergoymann C (1995) Fitting of diffusion-coefficients in a 3-compartment sustained-release drug formulation using a genetic algorithm. Int J Pharm 120(2):229–234

    Article  Google Scholar 

  110. Holland JH (1992) Adaptation in natural and artificial systems. MIT, Cambridge

    Google Scholar 

  111. Hosseini S, Farahani RZ, Dullaert W, Raa B, Rajabi M, Bolhari A (2014) A robust optimization model for a supply chain under uncertainty. IMA J Manag Math 25(4):387–402

    Article  MathSciNet  Google Scholar 

  112. Houck C, Joines J, Kay M, Wilson J (1997) Empirical investigation of the benefits of partial lamarckianism. Evol Comput 5(1):31–60

    Article  Google Scholar 

  113. Hsu CH (2007) Uplink MIMO-SDMA optimisation of smart antennas by phase-amplitude perturbations based on memetic algorithms for wireless and mobile communication systems. IET Commun 1(3):520–525

    Article  Google Scholar 

  114. Hsu CH, Shyr WJ (2008) Adaptive pattern nulling design of linear array antenna by phase-only perturbations using memetic algorithms. Commun Numer Methods Eng 24(11):1121–1133

    Article  MATH  Google Scholar 

  115. Hsu CH, Shyr WJ, Chen CH (2006) Adaptive pattern nulling design of linear array antenna by phase-only perturbations using memetic algorithms. In: Pan J, Shi P, Zhao Y (eds) First International Conference on Innovative Computing, Information and Control, vol 3 (ICICIC 2006). IEEE, Beijing, pp 308–311

    Google Scholar 

  116. Hsu CH, Chou PH, Shyr WJ, Chung YN (2007) Optimal radiation pattern design of adaptive linear array antenna by phase and amplitude perturbations using memetic algorithms. Int J Innov Comput Inf Control 3(5):1273–1287

    Google Scholar 

  117. Hsu CH, Shyr WJ, Ku KH, Chou PH (2008) Optimal radiation pattern design of adaptive linear phased array antenna using memetic algorithms. Int J Innov Comput Inf Control 4(9):2391–2403

    Google Scholar 

  118. Hsu CH, Shyr WJ, Kuo KH, Chou PH, Wu MJ (2010) Memetic algorithms for multiple interference cancellations of linear array based on phase-amplitude perturbations. J Optim Theory Appl 144(3):629–642

    Article  MathSciNet  MATH  Google Scholar 

  119. Hu M, Weir JD, Wu T (2014) An augmented multi-objective particle swarm optimizer for building cluster operation decisions. Appl Soft Comput 25:347–359

    Article  Google Scholar 

  120. Hu Z, Bao Y, Xiong T (2013) Electricity load forecasting using support vector regression with memetic algorithms. Sci World J 2013:article ID 292575

    Google Scholar 

  121. Hu Z, Bao Y, Xiong T (2014) Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression. Appl Soft Comput 25:15–25

    Article  Google Scholar 

  122. Iacca G, Neri F, Caraffini F, Suganthan PN (2014) A differential evolution framework with ensemble of parameters and strategies and pool of local search algorithms. In: Esparcia-Alcázar AI, Mora AM (eds) Applications of Evolutionary Computation. Lecture Notes in Computer Science, vol 8602. Springer, Berlin, pp 615–626

    Google Scholar 

  123. Ibaraki T (1997) Combination with dynamic programming. In: Bäck T, Fogel D, Michalewicz Z (eds) Handbook of Evolutionary Computation. Oxford University Press, New York NY, pp D3.4:1–2

    Google Scholar 

  124. Ihesiulor OK, Shankar K, Zhang Z, Ray T (2012) Delamination detection using methods of computational intelligence. In: Barsoum N, Faiman D, Vasant P (eds) Sixth Global Conference on Power Control and Optimization. AIP Conference Proceedings, vol 1499. American Institute of Physics, Las Vegas, pp 303–310

    Google Scholar 

  125. Ihesiulor OK, Shankar K, Zhang Z, Ray T (2014) Delamination detection with error and noise polluted natural frequencies using computational intelligence concepts. Compos B Eng 56:906–925

    Article  Google Scholar 

  126. Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans Evol Comput 7(2):204–223

    Article  Google Scholar 

  127. Jiao L, Gong M, Wang S, Hou B, Zheng Z, Wu Q (2010) Natural and remote sensing image segmentation using memetic computing. IEEE Comput Intell Mag 5(2):78–91

    Article  Google Scholar 

  128. Jiménez Laredo JL, Bouvry P, Lombraña González D, Fernández de Vega F, García Arenas M, Merelo Guervós JJ, Fernandes CM (2014) Designing robust volunteer-based evolutionary algorithms. Genet Program Evolvable Mach 15(3):221–244

    Article  Google Scholar 

  129. Jolai F, Tavakkoli-Moghaddam R, Rabiee M, Gheisariha E (2014) An enhanced invasive weed optimization for makespan minimization in a flexible flowshop scheduling problem. Scientia Iranica 21(3):1007–1020

    Google Scholar 

  130. Jones G, Willett P, Glen R, Leach A, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748

    Article  Google Scholar 

  131. Jones T (1995) Evolutionary algorithms, fitness landscapes and search. PhD thesis, University of New Mexico

    Google Scholar 

  132. Julstrom BA (1995) Very greedy crossover in a genetic algorithm for the traveling salesman problem. In: Proceedings of the 1995 ACM Symposium on Applied Computing. ACM, New York, pp 324–328

    Chapter  Google Scholar 

  133. Kalantzis G, Apte A, Radke R, Jackson A (2013) A reduced order memetic algorithm for constraint optimization in radiation therapy treatment planning. In: Takahashi S, Leo R (eds) 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking And Parallel/Distributed Computing (SNPD 2013). IEEE, Honolulu, pp 225–230

    Google Scholar 

  134. Kernighan B, Lin S (1972) An efficient heuristic procedure for partitioning graphs. Bell Syst J 49:291–307

    Article  MATH  Google Scholar 

  135. Khan SU, Qureshi IM, Zaman F, Shoaib B, Naveed A, Basit A (2014) Correction of faulty sensors in phased array radars using symmetrical sensor failure technique and cultural algorithm with differential evolution. Sci World J 2014:article ID 852539

    Google Scholar 

  136. Kim J, Kim CS, Geem ZW (2014) A memetic approach for improving minimum cost of economic load dispatch problems. Math Probl Eng 2014:article ID 906028

    Google Scholar 

  137. King C, Pendlebury DA (2013) Web of knowledge research frontiers 2013: 100 top ranked specialties in the sciences and social sciences. http://sciencewatch.com/sites/sw/files/sw-article/media/research-fronts-2013.pdf

  138. Klau GW (2009) A new graph-based method for pairwise global network alignment. BMC Bioinf 10(Suppl 1):S59

    Article  Google Scholar 

  139. Kleeman MP, Lamont GB, Cooney A, Nelson TR (2007) A multi-tiered memetic multiobjective evolutionary algorithm for the design of quantum cascade lasers. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Proceeedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2007). Lecture Notes in Computer Science, vol 4403. Springer, Berlin/Matsuhima, pp 186–200

    Chapter  Google Scholar 

  140. Kononova AV, Hughes KJ, Pourkashanian M, Ingham DB (2007) Fitness diversity based adaptive memetic algorithm for solving inverse problems of chemical kinetics. In: IEEE Congress on Evolutionary Computation. IEEE, Singapore, pp 2366–2373

    Google Scholar 

  141. Kononova AV, Ingham DB, Pourkashanian M (2008) Simple scheduled memetic algorithm for inverse problems in higher dimensions: application to chemical kinetics. In: IEEE Congress on Evolutionary Computation. IEEE, Hong Kong, pp 3905–3912

    Google Scholar 

  142. Krasnogor N (2004) Self generating metaheuristics in bioinformatics: the proteins structure comparison case. Genet Program Evolvable Mach 5(2):181–201

    Article  Google Scholar 

  143. Krasnogor N, Gustafson S (2004) A study on the use of “self-generation” in memetic algorithms. Nat Commun 3(1):53–76

    Article  MathSciNet  MATH  Google Scholar 

  144. Krasnogor N, Smith J (2008) Memetic algorithms: the polynomial local search complexity theory perspective. J Math Modell Algorithms 7(1):3–24

    Article  MathSciNet  MATH  Google Scholar 

  145. Krasnogor N, Blackburne B, Burke E, Hirst J (2002) Multimeme Algorithms for Protein Structure Prediction. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, pp 769–778

    Google Scholar 

  146. Krishna K, Ramakrishnan K, Thathachar M (1997) Vector quantization using genetic k-means algorithm for image compression. In: 1997 International Conference on Information, Communications and Signal Processing, vol 3. IEEE Press, New York, pp 1585–1587

    Google Scholar 

  147. Kumar JV, Kumar DMV (2014) Generation bidding strategy in a pool based electricity market using shuffled frog leaping algorithm. Appl Soft Comput 21:407–414

    Article  Google Scholar 

  148. Kumar PK, Sharath S, D’Souza RG, Chandra K (2007) Memetic nsga – a multi-objective genetic algorithm for classification of microarray data. In: 15th International Conference on Advanced Computing And Communications (ADCOM 2007). IEEE, Guwahati, pp 75–80

    Google Scholar 

  149. Kumle AN, Fathi SH, Broujeni ST (2014) Harmonic optimization in multi-level inverters by considering adjustable DC sources using memetic algorithm. In: 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2014). IEEE, Nakhon Ratchasima

    Google Scholar 

  150. Lam AYS, Li VOK (2012) Chemical reaction optimization: a tutorial. Memetic Comput 4(1):3–17

    Article  Google Scholar 

  151. Li YF, Pedroni N, Zio E (2013) A memetic evolutionary multi-objective optimization method for environmental power unit commitment. IEEE Trans Power Syst 28(3):2660–2669

    Article  Google Scholar 

  152. Liaw CF (2000) A hybrid genetic algorithm for the open shop scheduling problem. Eur J Oper Res 124:28–42

    Article  MathSciNet  MATH  Google Scholar 

  153. Liefooghe A, Verel S, Hao JK (2014) A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming. Appl Soft Comput 16:10–19

    Article  Google Scholar 

  154. Lim KK, Ong YS, Lim MH, Chen X, Agarwal A (2008) Hybrid ant colony algorithms for path planning in sparse graphs. Soft Comput 12(10):981–994

    Article  Google Scholar 

  155. Lin G, Zhu W, Ali MM (2014) A tabu search-based memetic algorithm for hardware/software partitioning. Math Probl Eng 2014:article ID 103059

    Google Scholar 

  156. Lin S, Kernighan B (1973) An effective heuristic algorithm for the traveling salesman problem. Oper Res 21:498–516

    Article  MathSciNet  MATH  Google Scholar 

  157. Linda O, Wijayasekara D, Manic M, McQueen M (2014) Optimal placement of phasor measurement units in power grids using memetic algorithms. In: 23rd IEEE International Symposium on Industrial Electronics (ISIE 2014). IEEE, Istanbul, pp 2035–2041

    Chapter  Google Scholar 

  158. Liu B, Wang L, Liu Y, Qian B, Jin YH (2010) An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes. Comput Chem Eng 34(4):518–528

    Article  Google Scholar 

  159. Liu S, Chen D, Wang Y (2014) Memetic algorithm for multi-mode resource-constrained project scheduling problems. J Syst Eng Electron 25(4):609–617

    Article  Google Scholar 

  160. Liu T, Jiang Z, Geng N (2014) A genetic local search algorithm for the multi-depot heterogeneous fleet capacitated arc routing problem. Flex Serv Manuf J 26(4, SI):540–564

    Google Scholar 

  161. Lorber D, Shoichet B (1998) Flexible ligand docking using conformational ensembles. Protein Sci 7(4):938–950

    Article  Google Scholar 

  162. Ma W, Huang Y, Li C, Liu J (2012) Image segmentation based on a hybrid immune memetic algorithm. In: Proceedings of the 2012 IEEE Congress on Evolutionary Computation, Brisbane, pp 1–8

    Google Scholar 

  163. Maheswaran R, Ponnambalam SG, Aranvidan C (2005) A meta-heuristic approach to single machine scheduling problems. Int J Adv Manuf Technol 25:772–776

    Article  Google Scholar 

  164. Marino A, Prügel-Bennett A, Glass CA (1999) Improving graph colouring with linear programming and genetic algorithms. In: Proceedings of EUROGEN 99, Jyväskylä, pp 113–118

    Google Scholar 

  165. Matei O, Pop PC, Sas JL, Chira C (2015) An improved immigration memetic algorithm for solving the heterogeneous fixed fleet vehicle routing problem. Neurocomputing 150(A, SI):58–66

    Google Scholar 

  166. Mendes A (2011) Identification of breast cancer subtypes using multiple gene expression microarray datasets. In: Wang D, Reynolds M (eds) 24th Australasian Joint Conference on Artificial Intelligence (AI 2011). Lecture Notes in Artificial Intelligence, vol 7106. Springer Berlin/Perth, pp 92–101

    Google Scholar 

  167. Mendes A, Cotta C, Garcia V, França P, Moscato P (2005) Gene ordering in microarray data using parallel memetic algorithms. In: Skie T, Yang CS (eds) Proceedings of the 2005 International Conference on Parallel Processing Workshops. IEEE Press, Oslo, pp 604–611

    Google Scholar 

  168. Mendes A, Boland N, Guiney P, Riveros C (2013) Switch and tap-changer reconfiguration of distribution networks using evolutionary algorithms. IEEE Trans Power Syst 28(1):85–92

    Article  Google Scholar 

  169. Mendoza M, Bonilla S, Noguera C, Cobos C, Leon E (2014) Extractive single-document summarization based on genetic operators and guided local search. Expert Syst Appl 41(9):4158–4169

    Article  Google Scholar 

  170. Merz P (2012) Memetic algorithms and fitness landscapes in combinatorial optimization. In: Neri F, Cotta C, Moscato P (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 95–119

    Chapter  Google Scholar 

  171. Merz P, Zell A (2002) Clustering Gene Expression Profiles with Memetic Algorithms. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, pp 811–820

    Google Scholar 

  172. Milojičić DS, Kalogeraki V, Lukose R, Nagaraja K, Pruyne J, Richard B, Rollins S, Xu Z (2002) Peer-to-peer computing. Technical report HPL-2002-57, Hewlett-Packard Labs

    Google Scholar 

  173. Molina D, Lozano M, García-Martínez C, Herrera F (2010) Memetic algorithms for continuous optimisation based on local search chains. Evol Comput 18(1):27–63

    Article  Google Scholar 

  174. Molina D, Lozano M, Sánchez AM, Herrera F (2011) Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-chains. Soft Comput 15(11):2201–2220

    Article  Google Scholar 

  175. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical report 826, California Institute of Technology, Pasadena

    Google Scholar 

  176. Moscato P (1993) An introduction to population approaches for optimization and hierarchical objective functions: the role of tabu search. Ann Oper Res 41(1–4):85–121

    Article  MATH  Google Scholar 

  177. Moscato P (1999) Memetic algorithms: a short introduction. In: Corne D, Dorigo M, Glover F (eds) New Ideas in Optimization. McGraw-Hill, London, pp 219–234

    Google Scholar 

  178. Moscato P (2012) Memetic algorithms: the untold story. In: Neri F, Cotta C, Moscato P (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 275–309

    Chapter  Google Scholar 

  179. Moscato P, Cotta C (2003) A gentle introduction to memetic algorithms. In: Glover F, Kochenberger G (eds) Handbook of Metaheuristics. Kluwer Academic Publishers, Boston, pp 105–144

    Chapter  Google Scholar 

  180. Moscato P, Norman MG (1992) A memetic approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passing systems. In: Valero M, Onate E, Jane M, Larriba JL, Suarez B (eds) Parallel Computing and Transputer Applications. IOS Press, Amsterdam, pp 177–186

    Google Scholar 

  181. Moscato P, Tinetti F (1992) Blending heuristics with a population-based approach: a memetic algorithm for the traveling salesman problem. Report 92–12, Universidad Nacional de La Plata

    Google Scholar 

  182. Moscato P, Mendes A, Berretta R (2007) Benchmarking a memetic algorithm for ordering microarray data. Biosystems 88(1–2):56–75

    Article  Google Scholar 

  183. Mozaffari A, Chehresaz M, Azad NL (2013) Component sizing of a plug-in hybrid electric vehicle powertrain, part a: coupling bio-inspired techniques to meshless variable-fidelity surrogate models. Int J Bio-Inspired Comput 5(6):350–383

    Article  Google Scholar 

  184. Mühlenbein H (1989) Parallel genetic algorithms, population genetics and combinatorial optimization. In: Schaffer (ed) 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, pp 416–421

    Google Scholar 

  185. Nagata Y, Kobayashi S (1997) Edge assembly crossover: a high-power genetic algorithm for the traveling salesman problem. In: Bäck T (ed) Seventh International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, pp 450–457

    Google Scholar 

  186. Nair SSK, Reddy NVS, Hareesha KS (2011) Exploiting heterogeneous features to improve in silico prediction of peptide status – amyloidogenic or non-amyloidogenic. BMC Bioinf 12(13):S21

    Article  Google Scholar 

  187. Nair SSK, Reddy NVS, Hareesha KS (2012) Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming peptide motifs. Protein Pept Lett 19(9):917–923

    Article  Google Scholar 

  188. Neri F (2012) Diversity management in memetic algorithms. In: Neri F, Cotta C, Moscato P (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 153–165

    Chapter  Google Scholar 

  189. Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol Comput 2:1–14

    Article  Google Scholar 

  190. Neri F, Mininno E (2010) Memetic compact differential evolution for Cartesian robot control. IEEE Comput Intell Mag 5(2):54–65

    Article  Google Scholar 

  191. Neri F, Toivanen J, Cascella GL, Ong YS (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE-ACM Trans Comput Biol Bioinform 4(2):264–278

    Article  Google Scholar 

  192. Neri F, Toivanen J, Makinen RAE (2007) An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV. Appl Intell 27(3):219–235

    Article  Google Scholar 

  193. Neri F, Cotta C, Moscato P (eds) (2012) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg

    Google Scholar 

  194. Nguyen QC, Ong YS, Kuo JL (2009) A hierarchical approach to study the thermal behavior of protonated water clusters H+(H2O)(n). J Chem Theory Comput 5(10):2629–2639

    Article  Google Scholar 

  195. Nikzad M, Farahani SSS, Tabar MB, Tourang H, Yousefpour B (2012) A new optimization method for pss design in New-England power system. Life Sci J Acta Zhengzhou Univ Overseas Ed 9(4):5478–5483

    Google Scholar 

  196. Nogueras R, Cotta C (2015) Studying fault-tolerance in Island-based evolutionary and multimemetic algorithms. J Grid Comput. doi:10.1007/s10723-014-9315-6

    MATH  Google Scholar 

  197. Noman N, Iba H (2007) Inferring gene regulatory networks using differential evolution with local search heuristics. IEEE-ACM Trans Comput Biol Bioinform 4(4):634–647

    Article  Google Scholar 

  198. Norman M, Moscato P (1989) A competitive and cooperative approach to complex combinatorial search. Technical report Caltech Concurrent Computation Program, Report. 790, California Institute of Technology, Pasadena. Expanded version published at the 20th Informatics and Operations Research Meeting, Buenos Aires (20th JAIIO), Aug 1991, pp 3.15–3.29

    Google Scholar 

  199. Montes de Oca MA, Cotta C, Neri F (2012) Local search. In: Neri F, Cotta C, Moscato P (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 29–41

    Chapter  Google Scholar 

  200. Oliveri G, Lizzi L, Pastorino M, Massa A (2012) A nested multi-scaling inexact-Newton iterative approach for microwave imaging. IEEE Trans Antennas Propag 60(2, 2):971–983

    Google Scholar 

  201. Olson BS, Shehu A (2012) Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface. Proteome Sci 10(1):S5

    Article  Google Scholar 

  202. Ong YS, Keane A (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99–110

    Article  Google Scholar 

  203. Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B: Cybern 36(1):141–152

    Article  Google Scholar 

  204. Ong YS, Lim MH, Chen X (2010) Memetic computation-past, present and future. IEEE Comput Intell Mag 5(2):24–31

    Article  Google Scholar 

  205. Ortega JC, Gimenez D, Alvarez-Melcon A, Quesada FD (2013) Hybrid metaheuristics for the design of coupled resonator filters. Appl Artif Intell 27(5):323–350

    Article  Google Scholar 

  206. Pal S, Basak A, Das S, Abraham A (2009) Linear antenna array synthesis with invasive weed optimization algorithm. In: Abraham A, Muda A, Herman N, Shamsuddin S, Huoy C (eds) International Conference of Soft Computing and Pattern Recognition. IEEE, Malacca, pp 161–166

    Google Scholar 

  207. Pal S, Basak A, Das S (2011) Linear antenna array synthesis with modified invasive weed optimisation algorithm. Int J Bio-Inspired Comput 3(4):238–251

    Article  Google Scholar 

  208. Palacios P, Pelta D, Blanco A (2006) Obtaining biclusters in microarrays with population-based heuristics. In: Rothlauf F (ed) Proceedings of Applications of Evolutionary Computing. Lecture Notes in Computer Science, vol 3907. Springer, Berlin/Budapest, pp 115–126

    Google Scholar 

  209. Pan QK, Dong Y (2014) An improved migrating birds optimisation for a hybrid flowshop scheduling with total flowtime minimisation. Inform Sci 277:643–655

    Article  MathSciNet  MATH  Google Scholar 

  210. Perales-Gravan C, Lahoz-Beltra R (2008) An AM radio receiver designed with a genetic algorithm based on a bacterial conjugation genetic operator. IEEE Trans Evol Comput 12(2):129–142

    Article  Google Scholar 

  211. Poikolainen I, Neri F (2013) Differential evolution with concurrent fitness based local search. In: Proceedings of the 2013 IEEE Congress on Evolutionary Computation. IEEE Press, Cancun, pp 384–391

    Google Scholar 

  212. Prodhon C, Prins C (2014) A survey of recent research on location-routing problems. Eur J Oper Res 238(1):1–17

    Article  MathSciNet  MATH  Google Scholar 

  213. Qin H, Zhang Z, Qi Z, Lim A (2014) The freight consolidation and containerization problem. Eur J Oper Res 234(1):37–48

    Article  MathSciNet  MATH  Google Scholar 

  214. Quevedo-Teruel O, Rajo-Iglesias E, Oropesa-Garcia A (2007) Hybrid algorithms for electromagnetic problems and the no-free-lunch framework. IEEE Trans Antennas Propag 55(3, 1):742–749

    Google Scholar 

  215. Quintero A, Pierre S (2003) Evolutionary approach to optimize the assignment of cells to switches in personal communication networks. Comput Commun 26(9):927–938

    Article  Google Scholar 

  216. Quintero A, Pierre S (2003) Sequential and multi-population memetic algorithms for assigning cells to switches in mobile networks. Comput Netw 43(3):247–261

    Article  MATH  Google Scholar 

  217. Radcliffe N (1994) The algebra of genetic algorithms. Ann Math Artif Intell 10:339–384

    Article  MathSciNet  MATH  Google Scholar 

  218. Radcliffe NJ, Surry PD (1994) Formal memetic algorithms. In: Fogarty TC (ed) AISB Workshop on Evolutionary Computing. Lecture Notes in Computer Science, vol 865. Springer, Berlin/Heidelberg, pp 1–16

    Google Scholar 

  219. Rager M, Gahm C, Denz F (2015) Energy-oriented scheduling based on evolutionary algorithms. Comput Oper Res 54:218–231

    Article  MathSciNet  MATH  Google Scholar 

  220. Rahiminejad A, Alimardani A, Vahidi B, Hosseinian SH (2014) Shuffled frog leaping algorithm optimization for AC-DC optimal power flow dispatch. Turk J Electr Eng Comput Sci 22(4):874–892

    Article  Google Scholar 

  221. Raja MAZ, Ahmad SuI, Samar R (2014) Solution of the 2-dimensional bratu problem using neural network, swarm intelligence and sequential quadratic programming. Neural Comput Appl 25(7–8):1723–1739

    Article  Google Scholar 

  222. Rao ARM, Lakshmi K (2012) Optimal design of stiffened laminate composite cylinder using a hybrid sfl algorithm. J Compos Mater 46(24):3031–3055

    Article  Google Scholar 

  223. Rao BS, Vaisakh K (2013) New variants/hybrid methods of memetic algorithm for solving optimal power flow problem with load uncertainty. Int J Hybrid Intell Syst (IJHIS) 10(3):117–128

    Article  Google Scholar 

  224. Richter H, Engelbrecht A (2014) Recent Advances in the Theory and Application of Fitness Landscapes, Emergence, Complexity and Computation, vol 6. Springer, Berlin/Heidelberg

    Book  Google Scholar 

  225. Rodríguez Rueda D, Cotta C, Fernández Leiva AJ (2011) A memetic algorithm for designing balanced incomplete blocks. Int J Comb Optim Probl Inform 2(1):14–22

    Google Scholar 

  226. Rothlauf F, Goldberg DE (2002) Representations for Genetic and Evolutionary Algorithms. Physica-Verlag, Heidelberg

    Book  MATH  Google Scholar 

  227. Salhi A, Rodriguez JAV (2014) Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions. Memetic Comput 6(2):77–84

    Article  Google Scholar 

  228. Santamaría J, Cordón O, Damas S, García-Torres JM, Quirin A (2009) Performance evaluation of memetic approaches in 3D reconstruction of forensic object. Soft Comput 13(8–9):883–904

    Article  Google Scholar 

  229. Sarmenta LF (1998) Bayanihan: web-based volunteer computing using java. In: Masunaga Y, Katayama T, Tsukamoto M (eds) Worldwide Computing and Its Applications – WWCA’98. Lecture Notes in Computer Science, vol 1368. Springer, Berlin/Heidelberg, pp 444–461

    Chapter  Google Scholar 

  230. Schaefer G (2014) Aco classification of thermogram symmetry features for breast cancer diagnosis. Memetic Comput (3):207–212

    Article  Google Scholar 

  231. Sevaux M, Dauzère-Pérès S (2003) Genetic algorithms to minimize the weighted number of late jobs on a single machine. Eur J Oper Res 151:296–306

    Article  MathSciNet  MATH  Google Scholar 

  232. Silva R, Berenguel M, Perez M, Fernandez-Garcia A (2014) Thermo-economic design optimization of parabolic trough solar plants for industrial process heat applications with memetic algorithms. Appl Energy 113(SI):603–614

    Google Scholar 

  233. Smith JE (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B Cybern 37(1):6–17

    Article  Google Scholar 

  234. Smith JE (2010) Meme fitness and memepool sizes in coevolutionary memetic algorithms. In: 2010 IEEE Congress on Evolutionary Computation. IEEE Press, Barcelona, pp 1–8

    Google Scholar 

  235. Smith JE (2012) Self-Adaptative and Coevolving Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 167–188

    Google Scholar 

  236. Sörensen K, Sevaux M (2006) MA | PM: memetic algorithms with population management. Comput OR 33:1214–1225

    Article  MATH  Google Scholar 

  237. Speer N, Merz P, Spieth C, Zell A (2003) Clustering gene expression data with memetic algorithms based on minimum spanning trees. In: IEEE Congress on Evolutionary Computation (CEC 2003), Canberra, pp 1848–1855

    Google Scholar 

  238. Speer N, Spieth C, Zell A (2004) A memetic clustering algorithm for the functional partition of genes based on the gene ontology. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, La Jolla, pp 252–259

    Google Scholar 

  239. Speer N, Spieth C, Zell A (2004) A memetic co-clustering algorithm for gene expression profiles and biological annotation. In: Congress on Evolutionary Computation (CEC 2004). IEEE, Portland, pp 1631–1638

    Google Scholar 

  240. Spieth C, Streichert F, Speer N, Zell A (2004) A memetic inference method for gene regulatory networks based on s-systems. In: 2004 IEEE Congress on Evolutionary Computation (CEC 2004), Portland, pp 152–157

    Google Scholar 

  241. Spieth C, Streichert F, Supper J, Speer N, Zell A (2005) Feedback memetic algorithms for modeling gene regulatory networks. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, La Jolla, pp 61–67

    MATH  Google Scholar 

  242. Steimel J, Engell S (2014) Conceptual design and optimisation of chemical processes under uncertainty by two-stage programming. In: Eden M, Siirola J, Towler G (eds) 8th International Conference on Foundations of Computer-Aided Process Design, vol 34. Elsevier Science BV, Cle Elum, pp 435–440

    Google Scholar 

  243. Streichert F, Tanaka-Yamawaki M (2006) The effect of local search on the constrained portfolio selection problem. In: IEEE Congress on Evolutionary Computation, Vancouver, pp 2353–2359

    Google Scholar 

  244. Sudholt D (2009) The impact of parametrization in memetic evolutionary algorithms. Theor Comput Sci 410(26):2511–2528

    Article  MathSciNet  MATH  Google Scholar 

  245. Sudholt D (2012) Parametrization and balancing local and global search. In: Neri F, Cotta C, Moscato P (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin/Heidelberg, pp 55–72

    Chapter  Google Scholar 

  246. Sun X, Wang Z, Zhang D (2008) A watermarking algorithm based on MA and DWT. In: IEEE International Symposium on Electronic Commerce and Security, Guangzhou, pp 916–919

    Google Scholar 

  247. Tanese R (1989) Distributed genetic algorithms. In: 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco, pp 434–439

    Google Scholar 

  248. Ting CK, Liao CC (2010) A memetic algorithm for extending wireless sensor network lifetime. Inform Sci 180(24):4818–4833

    Article  Google Scholar 

  249. Tirronen V, Neri F, Kärkkäinen T, Majava K, Rossi T (2008) An enhanced memetic differential evolution in filter design for defect detection in paper production. Evol Comput 16(4):529–555

    Article  Google Scholar 

  250. Tomassini M (2005) Spatially Structured Evolutionary Algorithms. Natural Computing Series. Springer, Berlin/New York

    MATH  Google Scholar 

  251. Tse S, Liang Y, Leung K, Lee K, Mok S (2005) Multi-drug cancer chemotherapy scheduling by a new memetic optimization algorithm. In: IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, pp 699–706

    Google Scholar 

  252. Tse SM, Liang Y, Leung KS, Lee KH, Mok TSK (2007) A memetic algorithm for multiple-drug cancer chemotherapy schedule optimization. IEEE Trans Syst Man Cybern B Cybern 37(1):84–91

    Article  Google Scholar 

  253. Urselmann M, Engell S (2010) Optimization-based design of reactive distillation columns using a memetic algorithm. In: Pierucci S, Ferraris B (eds) 20th European Symposium on Computer Aided Process Engineering. Elsevier Science BV, Ischia, vol 28, pp 1243–1248

    Chapter  Google Scholar 

  254. Urselmann M, Engell S (2015) Design of memetic algorithms for the efficient optimization of chemical process synthesis problems with structural restrictions. Comput Chem Eng 72:87–108

    Article  Google Scholar 

  255. Urselmann M, Sand G, Engell S (2009) A memetic algorithm for global optimization in chemical process synthesis. In: IEEE Congress on Evolutionary Computation (CEC 2009), Trondheim, pp 1721–1728

    Google Scholar 

  256. Urselmann M, Barkmann S, Sand G, Engell S (2011) A memetic algorithm for global optimization in chemical process synthesis problems. IEEE Trans Evol Comput 15(5, SI):659–683

    Google Scholar 

  257. Urselmann M, Barkmann S, Sand G, Engell S (2011) Optimization-based design of reactive distillation columns using a memetic algorithm. Comput Chem Eng 35(5):787–805

    Article  Google Scholar 

  258. Vesselinov VV, Harp DR (2012) Adaptive hybrid optimization strategy for calibration and parameter estimation of physical process models. Comput Geosci 49:10–20

    Article  Google Scholar 

  259. Vidal T, Crainic TG, Gendreau M, Prins C (2014) A unified solution framework for multi-attribute vehicle routing problems. Eur J Oper Res 234(3):658–673

    Article  MathSciNet  MATH  Google Scholar 

  260. Wang G, Wang J, Chen H (2014) Application of operator libraries on laminate strength optimization of composites. In: Yang G (ed) International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2013). Advanced Materials Research, vol 853. Trans Tech Publications Ltd, Hong Kong, pp 686–692

    Google Scholar 

  261. Wang L, Liu J (2013) A scale-free based memetic algorithm for resource-constrained project scheduling problems. In: Yin H, Tang K, Gao Y, Klawonn F, Lee M, Li B, Weise T, Yao X (eds) 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2013). Lecture Notes in Computer Science, vol 8206. Springer, Hefei, pp 202–209

    Chapter  Google Scholar 

  262. Wang L, Zheng DZ (2002) A modified genetic algorithm for job-shop scheduling. Int J Adv Manuf Technol 20:72–76

    Article  Google Scholar 

  263. Wang Y, Hao JK, Glover F, Lu Z (2014) A tabu search based memetic algorithm for the maximum diversity problem. Eng Appl Artif Intell 27:103–114

    Article  Google Scholar 

  264. Wanner EF, Guimaraes FG, Takahashi RHC, Lowther DA, Ramirez JA (2008) Multiobjective memetic algorithms with quadratic approximation-based local search for expensive optimization in electromagnetics. IEEE Trans Mag 44(6):1126–1129

    Article  Google Scholar 

  265. Whitley LD (1991) Fundamental principles of deception in genetic search. In: Rawlins G (ed) Foundations of Genetic Algorithms I. Morgan Kaufmann, San Mateo, pp 221–241

    Google Scholar 

  266. Widl M, Musliu N (2014) The break scheduling problem: complexity results and practical algorithms. Memetic Comput 6(2):97–112

    Article  Google Scholar 

  267. Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  268. Xu J, Yin Y, Cheng TCE, Wu CC, Gu S (2014) An improved memetic algorithm based on a dynamic neighbourhood for the permutation flowshop scheduling problem. Int J Prod Res 52(4):1188–1199

    Article  Google Scholar 

  269. Yang CH, Cheng YH, Chang HW, Chuang LY (2009) Primer design with specific PCr product size using memetic algorithm. In: IEEE Conference on Soft Computing in Industrial Applications (SMCIA 2008), Muroran, pp 332–337

    Google Scholar 

  270. Yang CH, Cheng YH, Chuang LY, Chang HW (2009) Specific PCr product primer design using memetic algorithm. Biotechnol Prog 25(3):745–753

    Article  Google Scholar 

  271. Yang S, Wang S, Liu Z, Wang M, Jiao L (2014) Improved bandelet with heuristic evolutionary optimization for image compression. Eng Appl Artif Intell 31(SI):27–34

    Google Scholar 

  272. Yang SH, Kiang JF (2014) Optimization of asymmetrical difference pattern with memetic algorithm. IEEE Trans Antennas Propag 62(4, 2):2297–2302

    Google Scholar 

  273. Zaman F, Qureshi IM, Munir F, Khan ZU (2014) Four-dimensional parameter estimation of plane waves using swarming intelligence. Chin Phys B 23(7):078402

    Article  Google Scholar 

  274. Zhao F, Tang J, Wang J, Jonrinaldi J (2014) An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem. Comput Oper Res 45:38–50

    Article  MathSciNet  MATH  Google Scholar 

  275. Zhou J, Ji Z, Zhu Z, He S (2014) Compression of next-generation sequencing quality scores using memetic algorithm. BMC Bioinform 15(15):S10

    Article  Google Scholar 

  276. Zhou M, Liu J (2014) A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks. Physica A-Stat Mech Appl 410:131–143

    Article  Google Scholar 

  277. Zhu GY, Zhang WB (2014) An improved shuffled frog-leaping algorithm to optimize component pick-and-place sequencing optimization problem. Expert Syst Appl 41(15):6818–6829

    Article  Google Scholar 

  278. Zhu Z, Ong YS (2007) Memetic algorithms for feature selection on microarray data. In: Liu D, Fei S, Hou Z, Zhang H, Sun C (eds) 4th International Conference on Advances in Neural Networks (ISNN 2007). Lecture Notes in Computer Science, vol 4491. Springer, Berlin/Nanjing, pp 1327–1335

    Chapter  Google Scholar 

  279. Zhu Z, Ong YS, Zurada JM (2010) Identification of full and partial class relevant genes. IEEE-ACM Trans Comput Biol Bioinform 7(2):263–277

    Article  Google Scholar 

  280. Zhu Z, Zhou J, Ji Z, Shi YH (2011) DNA sequence compression using adaptive particle swarm optimization-based memetic algorithm. IEEE Trans Evol Comput 15(5, SI):643–658

    Google Scholar 

  281. Zibakhsh A, Abadeh MS (2013) Gene selection for cancer tumor detection using a novel memetic algorithm with a multi-view fitness function. Eng Appl Artif Intell 26(4):1274–1281

    Article  Google Scholar 

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Acknowledgements

Carlos Cotta acknowledges support from the MINECO under the project EphemeCH (TIN2014-56494-C4-1-P), from the Junta de Andalucía under the project P10-TIC-6083 (DNEMESIS) and from the Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.

Pablo Moscato acknowledges support from the Australian Research Council Future Fellowship FT120100060 and Australian Research Council Discovery Projects DP120102576 and DP140104183 .

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Authors and Affiliations

  1. Dept. de Lenguajes y Ciencias de la Computación, Universidad de Málaga, ETSI Informtica, Bulevar Louis Pasteur, 35, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  2. Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, University of Newcastle, 1 Kookaburra Circuit, 2305, Newcastle, NSW, Australia

    Luke Mathieson

  3. School of Electrical Engineering and Computing, The University of Newcastle, University Drive, 2308, Newcastle, NSW, Australia

    Pablo Moscato

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  1. Carlos Cotta
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  2. Luke Mathieson
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  3. Pablo Moscato
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Correspondence to Carlos Cotta .

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Editors and Affiliations

  1. Department of Statistics and Operations, University of Valencia, Valencia, Spain

    Rafael Martí

  2. University of Florida Industrial and Systems Engineering, Gainesville, Florida, USA

    Pardalos Panos

  3. Modeling and Optimization Group, Amazon (United States) , Seattle, Washington, USA

    Mauricio G. C. Resende

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Cotta, C., Mathieson, L., Moscato, P. (2016). Memetic Algorithms. In: Martí, R., Panos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07153-4_29-1

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  • DOI: https://doi.org/10.1007/978-3-319-07153-4_29-1

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