Proceedings of the 5th International Conference on Genetic Algorithms
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This paper presents a mutation-based evolutionary algorithm that evolves genotypic genes for regulating developmental timing of phenotypic values. The genotype sequentially generates a given number of entire phenotypes and then finishes its life at each generation. Each genotypic gene represents a cycle time of changing probability to determine its corresponding phenotypic value in a life span of the genotype. This cycle time can be considered to be a sort of information on developmental timing. Furthermore, the algorithm has a learning mechanism for genotypic genes representing a long cycle time to change the probability more adaptively than those representing a short cycle time. Therefore, it can be expected that the algorithm brings different evolution speed to each phenotypic value. The experimental results show that the algorithm can identify building blocks of uniformly-scaled problems sequentially and also that a population size required for solving the problems is quite small but the number of function evaluations required is sub-exponential scale-up with the problem size.