Competing crossovers in an adaptive GA framework

Reports the results of experiments on multi-parent reproduction in an adaptive genetic algorithm (GA) framework. An adaptive mechanism based on competing subpopulations is incorporated into the algorithm in order to detect the best crossovers. Experiments on a number of test functions designed for s...

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Bibliographic Details
Main Authors: Eiben, A.E., Sprinkhuizen-Kuyper, I.G., Thijssen, B.A.
Format: Conference Proceeding
Language:English
Subjects:
Biological cells
Computer science
Encoding
Genetic algorithms
Genetic mutations
Helium
Interference
Production
Testing
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Summary:Reports the results of experiments on multi-parent reproduction in an adaptive genetic algorithm (GA) framework. An adaptive mechanism based on competing subpopulations is incorporated into the algorithm in order to detect the best crossovers. Experiments on a number of test functions designed for studying crossover performance show that multi-parent reproduction is superior to traditional two-parent crossover, but the adaptive mechanism is not able to reward better crossovers according to their performance. Nevertheless, the adaptive algorithm exhibits a performance that is comparable to the non-adaptive variant using the best crossover alone. This implies that it is sound and safe to use an adaptive GA with competing subpopulations/crossovers, instead of performing time-consuming comparisons in searching for the best operators.
DOI:10.1109/ICEC.1998.700152