Andres Felipe Cruz-Salinas
ABSTRACT
Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.
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- Beyer, H.G., Schwefel, H.P.: Evolution strategies-a comprehensive introduction. Natural computing 1(1), 3--52 (2002) Google Scholar
Digital Library
- Birattari, M., Yuan, Z., Balaprakash, P., Stützle, T.: F-race and iterated f-race: An overview. In: Experimental methods for the analysis of optimization algorithms, pp. 311--336. Springer (2010)Google Scholar
- Coelho, V., Coelho, I., Souza, M., Oliveira, T., Cota, L., Haddad, M., Mladenovic, N., Silva, R., Guimarães, F.: Hybrid self-adaptive evolution strategies guided by neighborhood structures for combinatorial optimization problems. Evolutionary computation 24(4), 637--666 (2016) Google ScholarDigital Library
- Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to algorithms second edition (2001) Google ScholarDigital Library
- De Leeuw, J., Mair, P.: Multidimensional scaling using majorization: Smacof in r. Department of Statistics, UCLA (2011)Google Scholar
- Eiben, Á.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on evolutionary computation 3(2), 124--141 (1999) Google ScholarDigital Library
- Gomez, J.: Self adaptation of operator rates in evolutionary algorithms. In: Genetic and Evolutionary Computation Conference, pp. 1162--1173. Springer (2004)Google ScholarCross Ref
- Hadka, D., Reed, P.: Borg: An auto-adaptive many-objective evolutionary computing framework. Evolutionary computation 21(2), 231--259 (2013)Google Scholar
- Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In: Evolutionary Computation, 1996., Proceedings of IEEE International Conference on. pp. 312--317. IEEE (1996)Google ScholarCross Ref
- Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection, vol. 1. MIT press (1992) Google ScholarDigital Library
- Koza, J.R.: Genetic programming ii: Automatic discovery of reusable subprograms. Cambridge, MA, USA (1994) Google ScholarDigital Library
- Koza, J.R., Andre, D., Bennett III, F.H., Keane, M.A.: Use of automatically defined functions and architecture-altering operations in automated circuit synthesis with genetic programming. In: Proceedings of the 1st annual conference on genetic programming, pp. 132--140. MIT Press (1996) Google ScholarDigital Library
- Kramer, O.: Evolutionary self-adaptation: a survey of operators and strategy parameters. Evolutionary Intelligence 3(2), 51--65 (2010)Google ScholarCross Ref
- López-Ibáñez, M., Dubois-Lacoste, J., Cáceres, L.P., Birattari, M., Stützle, T.: The irace package: Iterated racing for automatic algorithm configuration. Operations Research Perspectives 3, 43--58 (2016)Google ScholarCross Ref
- Maruo, M.H., Lopes, H.S., Delgado, M.R.: Self-adapting evolutionary parameters: encoding aspects for combinatorial optimization problems. In: European Conference on Evolutionary Computation in Combinatorial Optimization. pp. 154--165. Springer (2005) Google ScholarDigital Library
- Mitchell, M.: An introduction to genetic algorithms. MIT press (1998) Google ScholarDigital Library
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825--2830 (2011) Google ScholarDigital Library
- Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE transactions on evolutionary computation 1(1), 67--82 (1997) Google ScholarDigital Library
- Younes, A., Basir, O., Calamai, P., Areibi, S.: Adapting genetic algorithms for combinatorial optimization problems in dynamic environments. INTECH Open Access Publisher (2008)Google Scholar
- Zhang, K., Shasha, D.: Simple fast algorithms for the editing distance between trees and related problems. SIAM journal on computing 18(6), 1245--1262 (1989) Google ScholarDigital Library
Index Terms
- Self-adaptation of genetic operators through genetic programming techniques
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