Evolutionary programming with a simulated-conformist mutation strategy
- 158 Downloads
Evolutionary programming has been widely implemented as a continuous optimization algorithm. Prior studies have come to a bottleneck because most of the evolutionary programming algorithms are unable to robustly solve different types of optimization problems. We argue that such a bottleneck results from the existing mutation strategies’ making little use of the population information. Inspired by a psychological model which describes how a person optimizes his/her social activities by conformity behavior, this study proposes a variation vector of the mutation to simulate the conformity behavior with behavior-reference, majority-impact, and distinctive-impact factors. These factors, respectively, correspond with three types of population information for each mutated individual: heuristic information, optimal gradient, and population diversity. We use the proposed vector to design an improved evolutionary programming with a simulated-conformist mutation strategy. The results show that the population information produced by the three factors enhance the robustness of the performance of evolutionary programming in solving both uni- and multimodal functions. The finding is verified by empirical analyses of two sets of benchmark functions proposed in 1998 and 2013. The numerical results indicate that the proposed algorithm performs significantly better on average than the existing EPs and some other algorithms with similar strategies.
KeywordsContinuous optimization Evolutionary programming Simulated-conformist mutation
This work is supported by National Natural Science Foundation of China (61370102), Guangdong Natural Science Funds for Distinguished Young Scholar (2014A030306050), the Fundamental Research Funds for the Central Universities, SCUT (2015PT022), Guangdong High-Level Personnel of Special Support Program (2014TQ01X664), the Guangdong High-Level University Project Green Technologies for Marine Industries, the Science and Technology Planning Project of Guangdong Province (2013B011304002), and the Project of Educational Commission of Guangdong Province, China (2015KGJHZ014). The authors thank Mr. Changjian Xu for his help with the experiment.
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
- Anik M, Alam T, Ahmed S, Noman ASM, Rakibul Islam KM (2013) A dual mutation strategy embedded evolutionary programming for continuous optimization. In: World Congress on Nature and Biologically Inspired Computing (NABIC). IEEE, pp 84–91Google Scholar
- Bratton D, Kennedy J (2007) Defining a standard for particle swarm optimization. In: Swarm intelligence symposium. IEEE, pp 120–127Google Scholar
- Fogel DB (1991) System identification through simulated evolution: a machine learning approach to modeling. Ginn Press, BratislavaGoogle Scholar
- Fogel DB (1992) Evolving artificial intelligence. PhD dissertation, La Jolla, CA, USA. UMI order no. GAX93-03240Google Scholar
- Gämperle R, Müller SD, Koumoutsakos P (2002) A parameter study for differential evolution. Adv Intell Syst Fuzzy Syst Evol Comput 10:293–298Google Scholar
- Hansen N (2006) The CMA evolution strategy: a comparing review. In: Lozano J, Larranaga P, Inza I, Bengoetxea E (eds) Towards a new evolutionary computation. Advances on Estimation of Distribution Algorithms. Springer, Berlin, pp 75–102Google Scholar
- Hedar A-R, Fukushima M (2006) Directed evolutionary programming: towards an improved performance of evolutionary programming. In: IEEE Congress on Evolutionary Computation, 2006. CEC 2006. IEEE, pp 1521–1528Google Scholar
- Hong L, Drake JH, Özcan E (2014) A step size based self-adaptive mutation operator for evolutionary programming. In: Proceedings of the 2014 conference companion on genetic and evolutionary computation companion. ACM, pp 1381–1388Google Scholar
- Khatib W, Fleming PJ (1998) The Stud GA: a mini revolution? In: Parallel problem solving from nature-PPSN V. Springer, pp 683–691Google Scholar
- Liang JJ, Qu BY, Suganthan PN, Hernández-Dıaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical report, Computational Intelligence Laboratory 201212Google Scholar
- Rozenberg G, Thomas B, Kok JN (2011) Handbook of natural computing. Springer, BerlinGoogle Scholar
- Schwefel H-PP (1993) Evolution and optimum seeking: the sixth generation. Wiley, LondonGoogle Scholar
- Yao X, Liu Y (1998) Scaling up evolutionary programming algorithms. Evolutionary programming VII. Proc. of the seventh annual conference on evolutionary programming (EP98), Lecture Notes in Computer Science. Springer, Berlin, pp 103–112Google Scholar