A taxonomy for the crossover operator for real‐coded genetic algorithms: An experimental study

By Francisco Herrera, Manuel Lozano, A. M. Sánchez. International Journal of Intelligent Systems, 2003

406 citations recorded by the source corpus.

The main real‐coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models. © 2003 Wiley Periodicals, Inc.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/a10d84d36b9199aaca9dbec9817ec9b8176fa1ef

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Citations in the corpus

Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions (2011, methodology): “Evolutionary algorithms (EAs) such as genetic algorithms (GAs) [9,15,20,28, 58,59], differential evolution (DE) [7,43,46,53–55,64,67,74], particle swarm optimization (PSO) [21,22,38,61,70], and bacterial foraging optimization [6,12,13] are powerful tools for solving difficult optimization problems.”
Learning weighted linguistic rules to control an autonomous robot (2009, background): “genetic algorithms and allows different levels of exploita tion and exploration to be introduced simultaneously in the search process [ 14 ].”