Global optimization using a genetic algorithm with hierarchically structured population

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:261

Article

Toledo, C. F. M.¹  Oliveira, L.²  Franca, P. M.³ 

[1] Univ Sao Paulo, ICMC, BR-13566590 Sao Carlos, SP, Brazil

[2] Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP, Brazil

[3] Univ Estadual Paulista, BR-19060900 Presidente Prudente, SP, Brazil

关键词: Genetic algorithms;    Global optimization;    Continuous optimization;    Population set-based methods;    Hierarchical structure;   

DOI  :  10.1016/j.cam.2013.11.008

来源: Elsevier

【 摘 要 】

This paper applies a genetic algorithm with hierarchically structured population to solve unconstrained optimization problems. The population has individuals distributed in several overlapping clusters, each one with a leader and a variable number of support individuals. The hierarchy establishes that leaders must be fitter than its supporters with the topological organization of the clusters following a tree. Computational tests evaluate different population structures, population sizes and crossover operators for better algorithm performance. A set of known benchmark test problems is solved and the results found are compared with those obtained from other methods described in the literature, namely, two genetic algorithms, a simulated annealing, a differential evolution and a particle swarm optimization. The results indicate that the method employed is capable of achieving better performance than the previous approaches in regard as the two criteria usually employed for comparisons: the number of function evaluations and rate of success. The method also has a superior performance if the number of problems solved is taken into account. (C) 2013 Elsevier B.V. All rights reserved.

【 授权许可】

Free