Theoretical basis of parameter tuning for finding optima near the boundaries of search spaces in real-coded genetic algorithms |
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Authors: | Hiroshi Someya |
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Affiliation: | (1) The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan |
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Abstract: | Studies on parameter tuning in evolutionary algorithms are essential for achieving efficient adaptive searches. This paper
discusses parameter tuning in real-valued crossover operators theoretically. The theoretical analysis is devoted to improving
robustness of real-coded genetic algorithms (RCGAs) for finding optima near the boundaries of bounded search spaces, which
can be found in most real-world applications. The proposed technique for crossover-parameter tuning is expressed mathematically,
and thus enables us to control the dispersion of child distribution quantitatively. The universal applicability and effect
have been confirmed theoretically and verified empirically with five crossover operators. Statistical properties of several
practical RCGAs are also investigated numerically. Performance comparison with various parameter values has been conducted
on test functions with the optima placed not only at the center but also in a corner of the search space. Although the parameter-tuning
technique is fairly simple, the experimental results have shown the great effectiveness. |
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