Aprendizaje incremental basado en población como buena alternativa al uso de algoritmos genéticos

At present, new computational models that attempt to overcome to the classical optimization models have emerged, this is the case of Evolutionary Computation, which has been popularized by Genetic Algorithms and their different variants that promise to be better. In this article we will discuss the benefits and/or shortcomings of basic Genetic Algorithm and Population-Based Incremental Learning algorithm, which is an estimation of distributions 51 Research in Computing Science 116 (2016) pp. 51–64; rec. 2016-03-18; acc. 2016-05-10 algorithm and it is part of Evolutionary Computation ́s paradigm. A comparative study of both algorithms is presented, here it is established from the experimentation with 7 objective functions that the Population-Based Incremental Learning algorithm presents significant advantage on runtime of all experiments as well as the accuracy obtained in 6 of 7 objective functions analyzed. Although this advantage had already been reported, in this paper we have experimented with multimodal functions with two variables and three dimensions which are considered difficult to solve nowadays.

[1]  Reza Rastegar,et al.  The Population-Based Incremental Learning Algorithm converges to local optima , 2006, Neurocomputing.

[2]  Petros Koumoutsakos,et al.  Optimization based on bacterial chemotaxis , 2002, IEEE Trans. Evol. Comput..

[3]  Qiang Hua,et al.  Peak Detection Method for Multimodal Function Optimization , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[4]  Pengfei Guo,et al.  The enhanced genetic algorithms for the optimization design , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[5]  Carlos A. Coello Coello,et al.  Introduccion a los algoritmos geneticos , 1995 .

[6]  Shengxiang Yang,et al.  Population-Based Incremental Learning with Immigrants Schemes in Changing Environments , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[7]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[8]  Sangha Song,et al.  Multimodal Function Optimization Based on the Survival of the Fitness kind of the Evolution Strategy , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Ching-Shyang Chen,et al.  Approximation of multivariate functions and evaluation of particular solutions using Chebyshev polynomial and trigonometric basis functions , 2006 .

[10]  Debasish Ghose,et al.  Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions , 2009, Swarm Intelligence.

[11]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[12]  Isaac Siwale.  ON GLOBAL OPTIMIZATION , 2015 .

[13]  M. M. Raghuwanshi,et al.  A new distributed differential evolution algorithm , 2015, International Conference on Computing, Communication & Automation.

[14]  Michael N. Vrahatis,et al.  On the computation of all global minimizers through particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[15]  Xingxing Liu,et al.  An Improved Adaptive Immune Genetic Algorithm Based on Information Entropy , 2015, 2015 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration.

[16]  Francisco Herrera Triguero,et al.  La metaheurística de optimización basada en colonias de hormigas: modelos y nuevos enfoques , 2004 .

[17]  G. R. Kulkarni,et al.  Behavioral analysis of genetic algorithm for function optimization , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[18]  Nanlin Jin,et al.  Population based incremental learning with guided mutation versus genetic algorithms: iterated prisoners dilemma , 2005, 2005 IEEE Congress on Evolutionary Computation.