Towards a Genetic Algorithm for Function Optimization
نویسندگان
چکیده
This article analyses a version of genetic algorithm (GA, Holland 1975) designed for function optimization, which is simple and reliable for most applications. The novelty in current approach is random provision of parameters, created by the GA. Chromosome portions which do not t ranslate into fitness are given function to diversify control parameters for the GA, providing random parameter setting along the way, and doing away with fine-tuning of probabilities of crossover and mutation. We test our algorithm on Royal Road functions to examine the difference between our version (GAW) and the simple GA (SGA) in the speed of discovering schema and creating building blocks. We also look at the usefulness of other standard improvements, such as non-coding segments, elitist selection and multiple crossover.
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