GENITOR II: a distributed genetic algorithm

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Abstract
GENITOR is a genetic algorithm which employs one-at-a-time reproduction and allocates reproductive opportunities according to rank to achieve selective pressure. Theoretical arguments and empirical evidence suggest that GENITOR is less vulnerable to some of the biases that degrade performance in standard genetic algorithms. A distributed version of GENITOR which uses many smaller distributed populations in place of a single large population is introduced. GENITOR II is able to optimize a broad range of sample problems more accurately and more consistently than GENITOR with a single population. GENITOR II also appears to be more robust than a single population genetic algorithm, yielding better performance without parameter tuning. We present some preliminary analyses to explain the performance advantage of the distributed algorithm. A distributed search is shown to yield improved search on several classes of problems, including binary encoded feedforward neural networks, the Traveling Salesman Problem, and a set of ‘ deceptive problems’ specially designed to be hard for genetic algorithms.

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