Skip to main content
SpringerLink
Log in

A Survey on the Latest Development of Machine Learning in Genetic Algorithm and Particle Swarm Optimization

  • Chapter
  • First Online:
Optimization in Machine Learning and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

The concept of machine learning (ML) is becoming popular day by day among research community for many reasons—capability of solving high-dimensional real-world problem, availability of quality data to build and validate models and most importantly availability of required software supports. It has a great scope in several areas such as banking and financial services, government services, education, health care and transportation. The most important phases of any ML process are learning, also termed as training, and predicting. In order to match the outcome of the prediction with the available data, training of a ML model is required based on certain statistical and mathematical algorithms. In the next phase, accuracy of the predictions is judged based on certain statistical estimates and mathematical computations. Recently, interaction between ML concepts and various nature-inspired optimization techniques has received a great attention among researchers to solve real-world problems. The ML techniques are combined with nature-inspired optimization techniques to optimize the solution to solve any problem. In this chapter, work related to two well-known nature-inspired optimization algorithms, i.e. genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, applied in machine learning are presented and reviewed. The combined studies on both the approaches help the researchers to understand and apply similar approaches to identify optimized solution to a problem in future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abbass HA (2001) MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach. In: Proceedings of the 2001 Congress on evolutionary computation (IEEE Cat. No.01TH8546), 27–30 May 2001, IEEE, Seoul, South Korea

    Google Scholar 

  2. Abbeel P, Ng AY (2010) Inverse reinforcement learning. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, MA

    Google Scholar 

  3. Aihara, I. (2009): “Modeling synchronized calling behavior of Japanese tree frogs”, Physical Review E 80, 011918, pp 1–7

    Google Scholar 

  4. Azadeh A, Ghaderi SF, Tarverdian S, Saberi M (2007) Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. Appl Math Comput 186(2):1731–1741

    MathSciNet  MATH  Google Scholar 

  5. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  6. Baese AM, Schmid V (2014) Chapter 5—genetic algorithms. In: Pattern recognition and signal analysis in medical imaging 2nd edn. pp 135–149

    Google Scholar 

  7. Bhatnagar S, Prasad H, Prashanth L (2013) Algorithms for constrained optimization. In: Stochastic recursive algorithms for optimization, lecture notes in control and information sciences, 434, Springer, London, pp 167–186

    Google Scholar 

  8. Bamakan SMH, Amiric B, Mirzabagheri M, Sh Y (2015) A new intrusion detection approach using PSO based multiple criteria linear programming. Information technology and quantitative management (ITQM 2015). Procedia Computer Science 55:231–237

    Article  Google Scholar 

  9. Baghmisheh MTV, Madani K, Navarbaf A (2011) A discrete shuffled frog optimization algorithm. Artif Intell Rev 36–267

    Google Scholar 

  10. Baughman DR, Liu YA (1995) 2-Fundamental and practical aspects of neural computing. In: Neural networks in bioprocessing and chemical engineering, pp 21–109

    Google Scholar 

  11. Bandura A (1962) Social learning through imitation. In: Jones MR (ed), Nebraska symposium on motivation, University of Nebraska Press, Lincoln

    Google Scholar 

  12. Bellemare MG, Naddaf Y, Veness J, Bowling M (2015) An evaluation platform for general agents. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence, pp 4168–4152

    Google Scholar 

  13. Biyanto TR, Matradji, Irawan S, Febrianto HY, Afdanny N, Rahman AH, Gunawan KS, Pratama JAD, Bethiana TN (2017) Killer whale algorithm: an algorithm inspired by the life of killer whale. Procedia Computer Science 124:151–157

    Article  Google Scholar 

  14. Brockman G, Cheung V, Pettersson L, Schneider J, Schulman J, Tang J, Zaremba W (2016) OpenAI Gym. Machine Learning, arXiv: 1606.01540

    Google Scholar 

  15. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2:1–127

    Article  MATH  Google Scholar 

  16. Burton A, Vladimirova T (1997) Genetic algorithm utilising neural network fitness evaluation for musical composition. Artificial neural nets and genetic algorithms. In: ICANNGA 97, Proceedings of the 3rd international conference in Norwich, GB, April 2–4

    Google Scholar 

  17. Carbonne Y, Jacob C (2015) Genetic algorithm as machine learning for profiles recognition. In: 7th international joint conference on computational intelligence (IJCCI), 12–14 Nov. 2015, IEEE, Lisbon, Portugal

    Google Scholar 

  18. Chiroma H, Noor ASM, Abdulkareem S, Abubakar AI, Hermawan A, Qin H, Hamza MF, Herawa T (2017) Neural networks optimization through genetic algorithm searches: a review. Appl Mathe Info Sci II(6):1543–1564

    Article  Google Scholar 

  19. Chu S, Tsai P, Pan J (2006) Cat swarm optimization. In: Pacific rim international conference on artificial intelligence, part of the lecture notes in computer science, 4099, pp 854–858

    Google Scholar 

  20. Civicioglu P (2013) Artificial cooperative search algorithm for numerical optimization problems. Inf Sci 229:58–76

    Article  MATH  Google Scholar 

  21. Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144

    MathSciNet  MATH  Google Scholar 

  22. Dai Y, Xue B, Zhang M (2014) New representations in PSO for feature construction in classification. In: European conference on the applications of evolutionary computation, lecture notes in computer science. Springer, 8602, pp 476–488

    Google Scholar 

  23. Das S, Chowdhury A, Abraham A (2009) A bacterial evolutionary algorithm for automatic data clustering. IEEE congress on evolutionary computation, IEEE, Trondheim, Norway

    Google Scholar 

  24. Ding S, Xing WD, Zhao X, Wang L, Jia W (2019) A new asynchronous reinforcement learning algorithm based on improved parallel PSO. Appl Intell 1573–7497

    Google Scholar 

  25. Ding YR, Cai YJ, Sun PD, Chen B (2014) The use of combined neural networks and genetic algorithms for prediction of river water quality. J Appl Res Technol 12(3):493–499

    Article  Google Scholar 

  26. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theoret Comput Sci 344(2–3):243–278

    Article  MathSciNet  MATH  Google Scholar 

  27. Eglese RW (1990) Simulated annealing: a tool for operational research. European J Oper Res 46(3):15, 271–281

    Google Scholar 

  28. Emami H, Derakhshan F (2015) Election algorithm: a new socio-politically inspired strategy. AI Commun 28(3):591–603

    Article  MathSciNet  MATH  Google Scholar 

  29. Garro BA, Vázquez RA (2015) Designing artificial neural networks using particle swarm optimization algorithms. Comput Intell Neurosci (https://doi.org/10.1155/2015/369298)

  30. Gao L, Zhou C, Gao HB, Shi YR (2006) Combining particle swarm optimization and neural network for diagnosis of unexplained syncope. Int Conf Intell Comput Part Lecture Notes Comput Sci Book Series 4115:174–181

    Google Scholar 

  31. Gargari EA, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary computation, CEC, 2007 IEEE Congress, IEEE, Singapore, pp 4661–4667

    Google Scholar 

  32. Geem ZW (2010) State-of-the-art in the structure of harmony search algorithm. In: Recent advances in harmony search algorithm, studies in computational intelligence. Springer, Berlin, Heidelberg

    Google Scholar 

  33. Ghahramani Z (2003) Unsupervised learning. Summer school on machine learning. In: Advanced lectures on machine learning, lecture notes in computer science, 3176, pp 72–112

    Google Scholar 

  34. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2–3):95–99

    Article  Google Scholar 

  35. Goldberg, D.E., Deb, K. (1991): “A comparative analysis of selection schemes used in genetic algorithms”, in: Foundations of Genetic Algorithms, 1, Morgan Kaufmann Publishers Inc, pp. 69–93

    Google Scholar 

  36. Hamada M, Hassan M (2017) Artificial neural networks and particle swarm optimization algorithms for preference prediction in multi-criteria recommender systems. informatics 5(25):1–16

    Google Scholar 

  37. Han F, Yao HF, Ling QH (2011) An Improved extreme learning machine based on particle swarm optimization. Int Conf Intell Comput Bio-Inspired Comput Appl 6840:699–704

    Google Scholar 

  38. Harbi SH, Smith VJR (2006) Adapting k-means for supervised clustering. Appl Intell 24(3):219–226

    Article  Google Scholar 

  39. Harpham C, Dawson CW, Brown MR (2004) A review of genetic algorithms applied to training radial basis function networks. Neural Comput Appl 13(3):193–201

    Article  Google Scholar 

  40. Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S (2019) Emperor Penguins colony: a new metaheuristic algorithm for optimization. Evol Intel 12(2):211–226

    Article  Google Scholar 

  41. Hsieh JC, Chang PC, Chen SH (2006) Integration of genetic algorithm and neural network for financial early warning system: an example of Taiwanese Banking Industry. In: First international conference on innovative computing, information and control, 1, 30 Aug.–1 Sept. 2006, IEEE, Beijing, China

    Google Scholar 

  42. Hosseini HS (2009) The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int J Bio-Inspired Comput 1(1/2):71–79

    Article  Google Scholar 

  43. Huan TT, Kulkarni AJ, Kanesan J (2016) Ideology algorithm: a socio-inspired optimization methodology. Neural Comput Appl 1–32. (https://doi.org/10.1007/s00521-016-2379-4)

  44. Huang CH (2013) Engineering design by geometric programming. Mathematical problems in engineering, 2013, Article ID 568098, pp 1–8

    Google Scholar 

  45. Hussain K, Salleh MNM, Cheng S, Shi Y (2018) Metaheuristic research: a comprehensive survey. Artif Intell Rev (https://doi.org/10.1007/s10462-017-9605-z)

  46. Ibrahim AM, El-Amary NH (2018) Particle Swarm Optimization trained recurrent neural network for voltage instability prediction. J Electr Syst Inform Technol 5(2):216–228

    Article  Google Scholar 

  47. Indolia S, Goswami AK, Mishra SP, Asopa P (2018) Conceptual understanding of convolutional neural network-a deep learning approach. Procedia Comput Sci 132:679–688

    Article  Google Scholar 

  48. Iima H, Kuroe Y (2009) Swarm reinforcement learning algorithm based on particle swarm optimization whose personal bests have lifespans. In: International conference on neural information processing, part of the lecture notes in computer science book series, 5864, pp 169–178

    Google Scholar 

  49. Javid AA (2011) Anarchic society optimization: a human-inspired method. In: Evolutionary computation, CEC, 2011 IEEE Congress, IEEE, New Orleans, USA, pp 2586–2592

    Google Scholar 

  50. Jayasekara D (2018) Machine learning—particle swarm optimization (PSO) and Twitter, https://medium.com/pythondatasciencezerotohero/machine-learning-particle-swarm-optimization-pso-and-twitter-c952a9ace499

  51. Jones AJ (1993) Genetic algorithms and their applications to the design of neural networks. Neural Comput Appl 1(1):32–45

    Article  Google Scholar 

  52. Jolliffe IT (2002) Introduction. In: Principal component analysis, Springer series in statistics. Springer, New York, NY, pp 1–9

    Google Scholar 

  53. Joshi AS, Kulkarni O, Kakandikar GM, Nandedkar VM (2017) Cuckoo search optimization-a review. Mater Today: Proc 4(8):7262–7269

    Google Scholar 

  54. Jung SH (2003) Queen-bee evolution for genetic algorithms. Electron Lett 39(6):575–576

    Article  Google Scholar 

  55. Kashan AH (2009) League championship algorithm: a new algorithm for numerical function optimization. In: International conference on soft computing and pattern recognition, SOCPAR09, IEEE, Singapore, pp 43–48

    Google Scholar 

  56. Kaur R, Singh B, Gobindgarh I, Sahib BF, Sahib F (2011) A Hybrid neural approach for character recognition system. Int J Comput Sci Inform Technol 2(2):721–726

    Google Scholar 

  57. Kaboudan MA (2000) Genetic programming prediction of stock prices. Comput Econ 16(3):207–236

    Article  MATH  Google Scholar 

  58. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, 4, IEEE, pp 1942–1948

    Google Scholar 

  59. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39:459–471

    Article  MathSciNet  MATH  Google Scholar 

  60. Krausmann E, Cruz AM, Salzano E (2017) Chapter 14—reducing natech risk: organizational measures. Natech Risk Assessment and Management, Reducing the Risk of Natural-Hazard Impact on Hazardous Installations, pp 227–235

    Google Scholar 

  61. Krishnanand KN, Ghose D (2006) Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent Grid Syst Int J 2:209–222

    Article  MATH  Google Scholar 

  62. Kotsiantis SB (2013) Decision trees: a recent overview. Artif Intell Rev 39(4):261–283

    Article  Google Scholar 

  63. Kleining G, Witt H (2000) The Qualitative Heuristic approach: a methodology for discovery in psychology and the social sciences. Rediscovering the method of introspection as an example. Forum Q Soc Res 1(1), Article 13

    Google Scholar 

  64. Koehn, P. (1994): “Combining Genetic Algorithms and Neural Networks: The Encoding Problem”, A Thesis Presented for the Master of Science Degree The University of Tennessee, Knoxville, pp 1–67

    Google Scholar 

  65. Kumar M, Kulkarni AJ, Satapathy SC (2018) Socio evolution & learning optimization algorithm: a socio-inspired optimization methodology. Fut Generation Comput Syst 81:252–272

    Article  Google Scholar 

  66. Kulkarni AJ, Durugkar IP, Kumar M (2013) Cohort intelligence: a self supervised learning behavior. In: Systems, man, and cybernetics, SMC, IEEE international conference. IEEE, Manchester, UK, pp 1396–1400

    Google Scholar 

  67. Kumar V, Chhabra JK, Kumar D (2015) Differential search algorithm for multiobjective problems. Procedia Comput Sci 48:22–28

    Article  Google Scholar 

  68. Kuo HC, Lin CH (2013) Cultural evolution algorithm for global optimizations and its applications. J Appl Res Technol 11(4):510–522

    Article  Google Scholar 

  69. Lindfield GR, Penny JET (2012) 8—optimization methods. In: Numerical methods 3rd edn. Science Direct, pp 371–432

    Google Scholar 

  70. Lazovskiy V (2018) Travel time optimization with machine learning and genetic algorithm. Towards Data Science (https://towardsdatascience.com/travel-time-optimization-with-machine-learning-and-genetic-algorithm-71b40a3a4c2)

  71. Louveaux Q, Skutella M (2016) Integer programming and combinatorial optimization. In: 18th international conference, IPCO 2016, Liège, Belgium, June 1–3, 2016, proceedings, part of the lecture notes in computer science, 9682

    Google Scholar 

  72. Lee CV, Piramuthu S, Tsai YK (2010) Job shop scheduling with a genetic algorithm and machine learning. Int J Prod Res 35(4):1171–1191

    Article  MATH  Google Scholar 

  73. Man KF, Tang KS, Kwong S (1996) Genetic algorithms: concepts and applications [in engineering design]. IEEE Trans Industr Electron 43(5):519–534

    Article  Google Scholar 

  74. Mario ED, Talebpour Z, Martinoli A (2013) A Comparison of PSO and reinforcement learning for multi-robot obstacle avoidance. IEEE Congress on Evolutionary Computation, Cancún, México, June 20–23, 2013, pp 149–156

    Google Scholar 

  75. Marinakisa Y, Marinaki M (2014) A bumble bees mating optimization algorithm for the open vehicle routing problem. Swarm Evol Comput 15:80–94

    Article  Google Scholar 

  76. Min Hu M, Li W, Yan K, Ji Z, Hu H (2019) Modern machine learning techniques for univariate tunnel settlement forecasting: a comparative study. In: Mathematical problems in engineering, Hindawi, 2019, pp 1–12

    Google Scholar 

  77. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518:529–541

    Google Scholar 

  78. Moerland TM, Broekens J, Jonker CM (2018) Emotion in reinforcement learning agents and robots: a survey. Mach Learn 107(2):443–480

    Article  MathSciNet  MATH  Google Scholar 

  79. Moosavian N (2015) Soccer league competition algorithm for solving knapsack problems. Swarm Evol Comput 20:14–22

    Article  Google Scholar 

  80. Mousavi SS, Schukat M, Howley E (2016) Deep reinforcement learning: an overview. In: Proceedings of SAI intelligent systems conference, Lecture notes in networks and systems, 16, pp 426–440

    Google Scholar 

  81. Mulder WD, Bethard S, Moens MF (2015) A survey on the application of recurrent neural networks to statistical language modelling. Comput Speech Lang 30(1):61–98

    Article  Google Scholar 

  82. Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18

    Article  Google Scholar 

  83. Nazir M, Majid-Mirza A, Khan SA (2014) PSO-GA based optimized feature selection using facial and clothing information for gender classification. J Appl Res Technol 12(1):5–163

    Article  Google Scholar 

  84. Neto PSG, Petry GG, Aranildo RLJ, Ferreira TAE (2009) Combining artificial neural network and particle swarm system for time series forecasting. In: International joint conference on neural networks, IEEE, 14–19 June 2009, Atlanta, GA, USA

    Google Scholar 

  85. Ng AY (2006) Reinforcement learning and apprenticeship learning for robotic control. In: International conference on algorithmic learning theory, lecture notes in computer science, 4264, pp 29–31

    Google Scholar 

  86. Parpinelli RS, Lopes HS (2011) An eco-inspired evolutionary algorithm applied to numerical optimization. In: Third world congress on nature and biologically inspired computing, 19–21 Oct. 2011, IEEE, Salamanca, Spain

    Google Scholar 

  87. Pham D, Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi M (2005) The Bees algorithm technical note. Manufacturing Engineering Centre, Cardiff University, UK, pp 1–57

    Google Scholar 

  88. Poznyak TI,Oria IC, Poznyak AS (2019) Chapter 3—Background on dynamic neural networks. Ozonation and Biodegradation in Environmental Engineering, Dynamic Neural Network Approach, pp 57–74

    Google Scholar 

  89. Qolomany B, Maabreh M, Al-Fuqaha A, Gupta A, Benhaddou D (2017) Parameters optimization of deep learning models using particle swarm optimization. In: 13th international wireless communications and mobile computing conference (IWCMC), IEEE, 26–30 June 2017, Valencia, Spain

    Google Scholar 

  90. Rabanal P, Rodríguez I, Rubio F (2017) Applications of river formation dynamics. J Comput Sci 22:26–35

    Article  MathSciNet  Google Scholar 

  91. Rashedi E, pour HN, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  92. Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inform Sci 183(1):1–15

    Article  MathSciNet  Google Scholar 

  93. Robbins GE, Plumbley MD, Hughes JC, Fallside F, Prager R (1993) Generation and adaptation of neural networks by evolutionary techniques (GANNET). Neural Comput Appl 1(1):23–31

    Article  Google Scholar 

  94. Rosenberg L (2016) Artificial Swarm Intelligence vs human experts. In: International joint conference on neural networks (IJCNN), 24–29 July 2016, IEEE, Vancouver, BC, Canada

    Google Scholar 

  95. Sang HY, Duan PY, Li JQ (2018) An effective invasive weed optimization algorithm for scheduling semiconductor final testing problem. Swarm Evol Comput 38:42–53

    Article  Google Scholar 

  96. Satapathy S, Naik A (2016) Social group optimization (SGO): a new population evolutionary optimization technique. Complex Intel Syst 2(3):173–203

    Article  Google Scholar 

  97. Singh S, Singh AK (2018) Web-spam features selection using CFS-PSO. Procedia Comput Sci 125:568–575

    Article  Google Scholar 

  98. Silvaa GLF, Valente TLA, Silvaa AC, Paivaa ACD, Gattass M (2018) Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. Comput Methods Programs Biomed 162:109–118

    Article  Google Scholar 

  99. Schulman J, Levine S, Abbeel P, Jordan M, Moritz P (2015) Trust region policy optimization. In: Proceedings of the 32nd international conference on machine learning, PMLR, 37, pp 1889–1897

    Google Scholar 

  100. Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. arXiv preprint arXiv: 1707.06347

    Google Scholar 

  101. Serrano W (2019) Genetic and deep learning clusters based on neural networks for management decision structures. Neural Comput Appl 1–25 https://doi.org/10.1007/s00521-019-04231-8

  102. Serrano W (2018) The random neural network with a genetic algorithm and deep learning clusters. In Fintech: Smart Investment, Imperial College London

    Google Scholar 

  103. Shapiro AF (2002) The merging of neural networks, fuzzy logic, and genetic algorithms. Insurance Mathe Econ 31(1):115–131

    Article  MathSciNet  Google Scholar 

  104. Shafti LS, Pérez E (2004) Machine learning by multi-feature extraction using genetic algorithms. In: Ibero-American Conference on Artificial Intelligence, Part of the Lecture Notes in Computer Science, 3315, pp 246–255

    Google Scholar 

  105. Shi Y (2011) Brain storm optimization algorithm. In: International conference in Swarm Intelligence, advances in swarm intelligence, part of the lecture notes in computer science, 6728, pp 303–309

    Google Scholar 

  106. 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

    Article  MathSciNet  MATH  Google Scholar 

  107. Such FP, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J (2018) Deep Neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. Neural Evol Comput 1–14

    Google Scholar 

  108. Sun Y, Zhang M, Yen GG (2019) Automatically designing CNN architectures using genetic algorithm for image classification. arXiv: 1808.03818 v2, pp 1–12

    Google Scholar 

  109. Sun J, Zhang H, Zhang Q, Chen H (2018) Balancing exploration and exploitation in multiobjective evolutionary optimization. In: Proceedings of the genetic and evolutionary computation conference companion, GECCO’18, Kyoto, Japan—July 15–19, 2018

    Google Scholar 

  110. Suryansh S (2018) Genetic algorithms + neural networks = best of both worlds. Towards Data Science (https://towardsdatascience.com/gas-and-nns-6a41f1e8146d)

  111. Todorov E, Erez T, Tassa Y (2012) MuJoCo: A physics engine for model-based control. In: international conference on intelligent robots and systems (IROS) (https://doi.org/10.1109/iros.2012.6386109)

  112. Tian H, Pouyanfar S, Chen J, Chen SC, Iyengar SS (2018) Automatic convolutional neural network selection for image classification using genetic algorithms. In: IEEE international conference on information reuse and integration (IRI), 6–9 July 2018, IEEE, Salt Lake City, UT, USA

    Google Scholar 

  113. Venter G (2010) Review of optimization techniques. In: Encyclopedia of aerospace engineering. Wiley

    Google Scholar 

  114. Vizitiu I, Popescu F (2010) GANN system to optimize both topology and neural weights of a feed forward neural network. (https://doi.org/10.1109/iccomm.2010.5509105)

  115. Watanabe Y, Mizuguchi N, Fujii Y (1998) Solving optimization problems by using a Hopfield neural network and genetic algorithm combination. Syst Comput Japan 29:68–74

    Article  Google Scholar 

  116. Webb GJ (2010) Naïve Bayes. In: Encyclopedia of machine learning, pp 30–45

    Google Scholar 

  117. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  118. Wu B (1992) An introduction to neural networks and their applications in manufacturing. J Intell Manuf 3(6):391–403

    Article  Google Scholar 

  119. Xia X, Lo D, Wang X, Yang X, Li S, Sun J (2013) A comparative study of supervised learning algorithms for re-opened bug prediction. In: 17th European conference on software maintenance and reengineering, 5–8 March 2013, IEEE, Genova, Italy

    Google Scholar 

  120. Xiao G, Juan Z, Gao J (2015) Travel mode detection based on neural networks and particle Swarm optimization. Information 6:522–535

    Article  Google Scholar 

  121. Xu Y, Cui Z, Zeng J (2010) Social emotional optimization algorithm for nonlinear constrained optimization problems. In: Swarm, evolutionary, and memetic computing, SEMCCO 2010. In: Lecture notes in computer science, 6466, Springer, Berlin, Heidelberg, pp 583–590

    Google Scholar 

  122. Yang X (2010) A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Stud Comput Intell 284:65–74

    Google Scholar 

  123. Yang, X.S. (2012): “Flower Pollination Algorithm for Global Optimization”, International Conference on Unconventional Computing and Natural Computation, Part of the Lecture Notes in Computer Science, 7445, pp 240–249

    Google Scholar 

  124. Ye F (2017) Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data. Plos One, 12(12) (https://doi.org/10.1371/journal.pone.0188746)

  125. Youcai Z, Sheng H (2017) Chapter four—pollution characteristics of industrial construction and demolition waste. In: Pollution control and resource recovery, industrial construction and demolition wastes, pp 51–101

    Google Scholar 

  126. Zang H, Zhang S, Hapeshi K (2010) A review of nature-inspired algorithms. J Bionic Eng 7(Supplement):S232–S237

    Google Scholar 

  127. Zhang L, Wang L, Wang X, Liu K, Abraham A (2012) Research of neural network classifier based on FCM and PSO for breast cancer classification. In: International conference on hybrid artificial intelligence systems, part of the lecture notes in computer science book series, 7208, pp 647–654

    Google Scholar 

  128. Zhang Y (2012) Support vector machine classification algorithm and its application. Int Conf Info Comput Appl Commun Comput Info Sci 308:179–186

    Google Scholar 

  129. Zou J, Han Y, So SS (2008) Overview of artificial neural networks. In: DJ Livingstone (eds) Artificial neural networks, methods in molecular biology™, 458, Human Press

    Google Scholar 

  130. Zhang S, Zong M, Sun K, Liu Y, Cheng D (2014) “Efficient kNN algorithm based on graph sparse reconstruction. In: International conference on advanced data mining and applications, ADMA 2014, lecture notes in computer science, 8933, pp 356–369

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International (Deemed University), 412115, Pune, MH, India

    Dipti Kapoor Sarmah

Authors
  1. Dipti Kapoor Sarmah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dipti Kapoor Sarmah .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Symbiosis Institute of Technology, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sarmah, D.K. (2020). A Survey on the Latest Development of Machine Learning in Genetic Algorithm and Particle Swarm Optimization. In: Kulkarni, A., Satapathy, S. (eds) Optimization in Machine Learning and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0994-0_6

Download citation

Publish with us

Policies and ethics

Search

Navigation