An Empirical Comparison of Seven Iterative and Evolutionary Heuristics for Static Function Optimization

Abstract

This report is a summary of the results obtained from a large scale empirical comparison of seven iterative and evolution-based optimization heuristics. Twenty-seven static optimization problems, spanning six sets of problem classes which are commonly explored in genetic algorithm literature, are exumined. The search spaces in these problems range from 2300 to 22040. The results indicate that using standard genetic algorithms for the optimization of staticfunctions does not yield a benefit, in terms of thefinal answer obtained, over simpler optimization heuristics.