Chun Wai Liew and Mayank Lahiri, Lafayette College
Genetic algorithm based optimizers have to balance extensive exploration of solution spaces to find good solutions with convergence to generate solutions quickly. Many optimizers use a two phase approach where the first phase explores the solution space and the second converges on a set of potential regions. This paper describes a meta-level algorithm (GA_ITER) that iteratively applies a GA based optimizer with a bias towards either exploration or convergence. The optimizer is executed with a very small number of evaluations which leads to fast generation of solutions. The iterative approach of GA_ITER has been shown to lead to fast generation of good solutions. Experiments in problems from two real-world domains have shown that GA_ITER can improve the performance of an existing GA without compromising the quality of the solution.