A fitness estimation strategy for genetic algorithms

Abstract

Genetic Algorithms (GAs) are a popular and robust strategy for optimisation problems. However, these algorithms often require huge computation power for solving real problems and are often criticized for their slow operation. For most applications, the bottleneck of the GAs is the fitness evaluation task. This paper introduces a fitness estimation strategy (FES) for genetic algorithms that does not evaluate all new individuals, thus operating faster. A fitness and associated reliability value are assigned to each new individual that is only evaluated using the true fitness function if the reliability value is below some threshold. Moreover, applying some random evaluation and error compensation strategies to the FES further enhances the performance of the algorithm. Simulation results show that for six optimization functions, the GA with FES requires fewer evaluations while obtaining similar solutions to those found using a traditional genetic algorithm. For these same functions the algorithm generally also finds a better fitness value on average for the same number of evaluations. Additionally the GA with FES does not have the side effect of premature convergence of the population. It climbs faster in the initial stages of the evolution process without becoming trapped in the local minima.