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Performance of a bell-curve based evolutionary optimization algorithm
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DOI：10.1007/s001580100103.png)
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Abstract
En 中文
An evolutionary search strategy utilizing two normal distributions to generate children is presented. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu+mu) evolutionary strategies but with fewer parameters to adjust. Extensive tests regarding the sensitivity of BCB parameters to performance are provided. The test suite includes continuous variable constrained hub design problems, mixed discrete and continuous variable constrained hub design problems, and an unconstrained highly multimodal discrete optimization problem.
Keywords:
evolutionary algorithm
heuristic
optimization
applications
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