Global optimization using a genetic algorithm with hierarchically structured population

• Autores: C. F. M. Toledo, L. Oliveira, P. M. França
• Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 261, Nº 1, 2014, págs. 341-351
• Idioma: inglés
• DOI: 10.1016/j.cam.2013.11.008
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• Resumen

  • 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.