Publication: Global optimization using a genetic algorithm with hierarchically structured population
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Undergraduate course
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Elsevier B.V.
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Article
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Abstract
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.
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Keywords
Genetic algorithms, Global optimization, Continuous optimization, Population set-based methods, Hierarchical structure
Language
English
Citation
Journal Of Computational And Applied Mathematics. Amsterdam: Elsevier Science Bv, v. 261, p. 341-351, 2014.
