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Evolutionary Algorithms

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Handbook of Heuristics

Abstract

Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by aspects of natural evolution. Modern varieties incorporate a broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic engineering concerns; however, all EAs essentially operate by maintaining a population of potential solutions and in some way artificially ‘evolving’ that population over time. Particularly well-known categories of EAs include genetic algorithms (GAs), Genetic Programming (GP), and Evolution Strategies (ES). EAs have proven very successful in practical applications, particularly those requiring solutions to combinatorial problems. EAs are highly flexible and can be configured to address any optimization task, without the requirements for reformulation and/or simplification that would be needed for other techniques. However, this flexibility goes hand in hand with a cost: the tailoring of an EA’s configuration and parameters, so as to provide robust performance for a given class of tasks, is often a complex and time-consuming process. This tailoring process is one of the many ongoing research areas associated with EAs.

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References

  1. Lones MA (2014) Metaheuristics in nature-inspired algorithms. In: Proceedings of genetic and evolutionary computation conference (GECCO 2014), workshop on metaheuristic design patterns (MetaDeeP). ACM, pp 1419–1422

    Google Scholar 

  2. Fogel DB (1998) Evolutionary computation: the fossil record. Wiley-IEEE Press, Piscataway

    Google Scholar 

  3. Hauschild M, Pelikan M (2011) An introduction and survey of estimation of distribution algorithms. Swarm Evol Comput 1(3):111–128. https://doi.org/10.1016/j.swevo.2011.08.003. Available: http://www.sciencedirect.com/science/article/pii/S2210650211000435

    Article  Google Scholar 

  4. Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31. https://doi.org/10.1109/TEVC.2010.2059031. Available: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5601760&url=http{%}3A{%}2F{%}2Fieeexplore.ieee.org{%}2Fxpls{%}2Fabs\~all.jsp{%}3Farnumber{%}3D5601760

    Article  Google Scholar 

  5. Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/1008202821328. Available: http://link.springer.com/article/10.1023%2FA%3A1008202821328#page-1.

  6. Zaharie D (2009) Influence of crossover on the behavior of differential evolution algorithms. Appl Soft Comput 9(3):1126–1138. https://doi.org/10.1016/j.asoc.2009.02.012. Available: http://www.sciencedirect.com/science/article/pii/S1568494609000325

    Article  Google Scholar 

  7. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644. https://doi.org/10.1007/s10732-008-9080-4. Available: http://link.springer.com/article/10.1007/s10732-008-9080-4

  8. Hansen N, Auger A, Finck S, Ros R (2010) Real-parameter black-box optimization benchmarking 2010: experimental setup. INRIA research report No. 7215. INRIA

    Google Scholar 

  9. Liang J, Qu B, Suganthan P, Hernández-Díaz A (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical report 201212. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, pp 3–18

    Google Scholar 

  10. Tang K, Li X, Suganthan PN, Yang Z, Weise T (2009) Benchmark functions for the CEC’2010 special session and competition on large-scale global optimization. Technical report. Nature Inspired Computation and Applications Laboratory, University of Science and Technology of China

    Google Scholar 

  11. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893. Available: http://ieeexplore.ieee.org/xpls/abs~all.jsp?arnumber=585893

    Article  Google Scholar 

  12. Igel C, Toussaint M (2003) On classes of functions for which no free lunch results hold. Inf Process Lett 86(6):317–321

    Google Scholar 

  13. Piotrowski AP (2015) Regarding the rankings of optimization heuristics based on artificially-constructed benchmark functions. Inf Sci 297:191–201. Available: http://www.sciencedirect.com/science/article/pii/S0020025514010937

  14. Lones MA, Tyrrell AM (2007) Regulatory motif discovery using a population clustering evolutionary algorithm. IEEE/ACM Trans Comput Biol Bioinform 4(3):403–414. https://doi.org/10.1109/tcbb.2007.1044. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4288066

    Article  Google Scholar 

  15. Koza J (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    Google Scholar 

  16. Miller JF (2011) Cartesian genetic programming. https://doi.org/10.1007/978-3-642-17310-3_2

    Chapter  Google Scholar 

  17. Veenhuis CB (2009) Tree based differential evolution. Lect Notes Comput Sci 5481:208–219

    Google Scholar 

  18. Kim K, Shan Y, Nguyen X, McKay RI (2014) Probabilistic model building in genetic programming: a critical review. Genet Program Evolvable Mach 15(2):115–167. https://doi.org/10.1007/s10710-013-9205-x. Available: http://link.springer.com/article/10.1007/s10710-013-9205-x

    Article  Google Scholar 

  19. Poli R, Langdon W, McPhee NF (2008) A field guide to genetic programming. Published via http://lulu.com

  20. Luke S (2013) Essentials of metaheuristics. Published via http://lulu.com

  21. Stanley KO, Miikkulainen R (2003) A taxonomy for artificial embryogeny. Artif Life 9(2):93–130. https://doi.org/10.1162/106454603322221487. Available: http://www.mitpressjournals.org/doi/abs/10.1162/106454603322221487 (pages 94 and 95)

    Article  Google Scholar 

  22. Floreano D, Dürr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intell 1(1):47–62. https://doi.org/10.1007/s12065-007-0002-4. Available: http://link.springer.com/article/10.1007/s12065-007-0002-4

    Article  Google Scholar 

  23. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program, C3P report 826

    Google Scholar 

  24. Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol Comput 2:1–14. https://doi.org/10.1016/j.swevo.2011.11.003. Available: http://www.sciencedirect.com/science/article/pii/S2210650211000691

    Article  Google Scholar 

  25. Hao J (2012) Memetic algorithms in discrete optimization. In: Neri F, Cotta C, Moscato P (eds) Handbook of memetic algorithms. Springer, Berlin/Heidelberg. https://doi.org/10.1007/978-3-642-23247-3_6

    Google Scholar 

  26. Ross P (2005) Hyper-heuristics. In: Search methodologies. Springer, Berlin, pp 529–556

    Google Scholar 

  27. Singh G, Deb K (2006) Comparison of multi-modal optimization algorithms based on evolutionary algorithms. ACM, New York. https://doi.org/10.1145/1143997.1144200

  28. Mengshoel OJ, Goldberg DE (2008) The crowding approach to niching in genetic algorithms. Evol Comput 16(3):315–354. https://doi.org/10.1162/evco.2008.16.3.315. Available: http://www.mitpressjournals.org/doi/abs/10.1162/evco.2008.16.3.315

  29. Sareni B, Krahenbuhl L (1998) Fitness sharing and niching methods revisited. IEEE Trans Evol Comput 2(3):97–106. https://doi.org/10.1109/4235.735432. Available: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=735432&url=http{%}3A{%}2F{%}2Fieeexplore.ieee.org{%}2Fiel4{%}2F4235{%}2F15834{%}2F00735432.pdf{%}3Farnumber{%}3D735432

  30. Lim T (2014) Structured population genetic algorithms: a literature survey. Artif Intell Rev 41(3):385–399. https://doi.org/10.1007/s10462-012-9314-6. Available: http://link.springer.com/article/10.1007%2Fs10462-012-9314-6

    Article  Google Scholar 

  31. Shir OM, Back T (2005) Dynamic niching in evolution strategies with covariance matrix adaptation. https://doi.org/10.1109/CEC.2005.1555018

  32. Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  33. Knowles J, Corne D (1999) The pareto archived evolution strategy: a new baseline algorithm for paretomultiobjective optimisation. In: Proceedings of the 1999 congress on evolutionary computation (CEC’99), vol 1. IEEE

    Google Scholar 

  34. Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  35. Corne DW, Deb K, Fleming PJ, Knowles JD (2003) The good of the many outweighs the good of the one: evolutionary multi-objective optimization. IEEE Connect Newslett 1(1):9–13

    Google Scholar 

  36. Zhou A, Qu B, Li H, Zhao S, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1(1):32–49. https://doi.org/10.1016/j.swevo.2011.03.001. Available: http://www.sciencedirect.com/science/article/pii/S2210650211000058

    Article  Google Scholar 

  37. Goldberg D, Smith R (1987) Nonstationary function optimization using genetic algorithm with dominance and diploidy. In: Proceedings of the second international conference on genetic algorithms and their application (ICGA). Laurence Erlbaum Associates, pp 59–68

    Google Scholar 

  38. Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6:1–24. https//doi.org/10.1016/j.swevo.2012.05.001. Available: http://www.sciencedirect.com/science/article/pii/S2210650212000363

    Article  Google Scholar 

  39. Popovici E, Bucci A, Wiegand RP, De Jong ED (2012) Coevolutionary principles. In: Rozenberg G, Bäck T, Kok JN (eds) Handbook of natural computing. Springer, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_31

    Google Scholar 

  40. Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D Nonlinear Phenom 42(1–3):228–234. https://doi.org/10.1016/0167-2789(90)90076-2. Available: http://www.sciencedirect.com/science/article/pii/0167278990900762

    Article  Google Scholar 

  41. Potter MA, Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29. https://doi.org/10.1162/106365600568086. Available: http://www.mitpressjournals.org/doi/abs/10.1162/106365600568086

    Article  Google Scholar 

  42. Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999. https://doi.org/10.1016/j.ins.2008.02.017. Available: http://www.sciencedirect.com/science/article/pii/S002002550800073X

    Article  MathSciNet  Google Scholar 

  43. Urbanowicz RJ, Moore JH (2009) Learning classifier systems: a complete introduction, review, and roadmap. J Artif Evol Appl 2009:1–25

    Article  Google Scholar 

  44. Ochoa G, Harvey I, Buxton H (1999) On recombination and optimal mutation rates. In: Proceedings of genetic and evolutionary computation conference, vol 1, pp 488–495. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.2369

    Google Scholar 

  45. Eiben AE, Smit SK (2011) Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol Comput 1(1):19–31. https://doi.org/10.1016/j.swevo.2011.02.001. Available: http://www.sciencedirect.com/science/article/pii/S2210650211000022

    Article  Google Scholar 

  46. Fogel LJ (1962) Autonomous automata. Ind Res 4(2):14–19

    Google Scholar 

  47. Ochoa G, Blum C, Chicano F (2015) Evolutionary computation in combinatorial optimization. Springer International Publishing: Imprint: Springer, Cham

    Google Scholar 

  48. Bajpai RP (ed) (2014) Innovative design, analysis and development practices in aerospace and automotive engineering: I-Dad 2014, 22–24 Feb 2014. Springer Science & Business, Singapore

    Google Scholar 

  49. Gaurav A, Kumar V, Nigam D (2012) New applications of soft computing in bioinformatics: a review. J Pure Appl Sci Tech 11(1):12–22

    Google Scholar 

  50. Gupta SK, Ramteke M (2014) Applications of genetic algorithms in chemical engineering II: case studies. In: Applications of metaheuristics in process engineering. Springer, Cham, pp 61–87

    Google Scholar 

  51. Bentley P, Corne D (2002) Creative evolutionary systems. Morgan Kaufmann, San Francisco

    Google Scholar 

  52. Chen SH (ed) (2012) Genetic algorithms and genetic programming in computational finance. Springer Science & Business Media, New York

    Google Scholar 

  53. Gen M, Cheng R (1996) Genetic algorithms and manufacturing systems design, 1st edn. Wiley, New York

    Book  Google Scholar 

  54. Adeli H, Sarma KC (2006) Cost optimization of structures: fuzzy logic, genetic algorithms, and parallel computing. Wiley, Chichester

    Book  Google Scholar 

  55. Lones MA, Tyrrell AM (2007) A co-evolutionary framework for regulatory motif discovery. https://doi.org/10.1109/CEC.2007.4424978

  56. Lones M, Alty JE, Lacy SE, Jamieson DR, Possin KL, Schuff N, Smith SL (2013) Evolving classifiers to inform clinical assessment of parkinson’s disease. In: 2013 IEEE symposium on computational intelligence in healthcare and e-health (CICARE), pp. 76–82. IEEE

    Google Scholar 

  57. Lones M, Turner AP, Caves LS, Stepney S, Smith SL, Tyrrell AM (2014) Artificial biochemical networks: evolving dynamical systems to control dynamical systems. IEEE Trans Evol Comput 18(2):145–166

    Article  Google Scholar 

  58. Lones MA, Smith SL, Tyrrell AM, Alty JE, Jamieson DS (2013) Characterising neurological time series data using biologically motivated networks of coupled discrete maps. BioSystems 112(2):94–101

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Heriot-Watt University, Edinburgh, UK

    David Corne & Michael A. Lones

Authors
  1. David Corne
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  2. Michael A. Lones
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Corresponding author

Correspondence to David Corne .

Editor information

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. Amazon.com, Inc+University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

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Corne, D., Lones, M.A. (2018). Evolutionary Algorithms. In: Martí, R., Panos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07153-4_27-1

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  • DOI: https://doi.org/10.1007/978-3-319-07153-4_27-1

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