An Efficient Genetic Algorithm for Optimization Problems with Time-Consuming Fitness Evaluation
Abstract
In classical genetic algorithm, fitness evaluations are often very expensive or highly time-consuming, especially for some engineering optimization problems. We present an efficient genetic algorithm (GA) by combining clustering methods with an empirical fitness estimating formula. The new individuals are clustered at first, and then only the cluster representatives are really evaluated by its original time-consuming fitness computing processes, and other individuals undergo high efficient fitness evaluating processes by using the empirical fitness estimating formula. To further improve the accuracy of fitness estimations, we present a schema discovery strategy by extracting the common encoding characters from both high-fitness individual group and low-fitness individual group, and then adjust the estimated fitness for each individual based on the matching with the discovered schema. Experiments show that the schema discovery strategy contributes remarkably to the accuracy of fitness estimation. Numerical experiments of some well-known benchmark problems and a practical engineering problem demonstrate that the proposed method could improve the efficiency by over 30% in terms of the times of real fitness evaluations at the similar optimization accuracy of classical genetic algorithm.
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