A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Theoretical Analysis of Simplex Crossover for Real-Coded Genetic Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Fuzzy Recombination for the Breeder Genetic Algorithm
Proceedings of the 6th International Conference on Genetic Algorithms
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Self-adaptive simulated binary crossover for real-parameter optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Most real-parameter genetic algorithms (RGAs) use a blending of participating parent solutions to create offspring solutions in its recombination operator. The blending operation creates solutions either around one of the parent solutions (having a parent-centric approach) or around the centroid of the parent solutions (having a mean-centric approach). In this paper, we argue that a self-adaptive approach in which a parent or a mean-centric approach is adopted based on population statistics is a better procedure than either approach alone. We propose a self-adaptive simulated binary crossover (SA-SBX) approach for this purpose. On a test suite of six unimodal and multi-modal test problems, we demonstrate that a RGA with SA-SBX approach performs consistently better in locating the global optimum solution than RGA with original SBX operator and RGA with mean-centric SBX operator.