The acceleration of evolutionary computations using fitness estimation

Evolutionary computation (EC) is widely applied to various kinds of combinatorial optimization problems. ECs are generally time-consuming because they need much trial and error. To accelerate ECs, some modification methods of the genetic operator have been proposed, such as improving mutation and recombination of chromosomes and/or their control parameters and so on. Through these modifications, ECs can find the suboptimal solutions in the relatively early generations. In spite of these improvements, ECs still require much time to obtain the solution. In many engineering applications of ECs, fitness evaluation spent the most computational time. This paper presents a new approach for the acceleration of ECs by reducing the time for fitness evaluation. Saving the time for fitness evaluation results in accelerating the ECs in the time domain. In the proposed method, only one individual of the population is actually evaluated in each generation. Fitness values for the rest of the population are estimated with simple calculation. Although the errors of estimation may decelerate the ECs in the generation domain, saving time in the evaluation scheme will exceed the deceleration. As a result, we can obtain a suboptimal solution relatively faster. The simulation results of designing the fuzzy logic controller using GA shows the effectiveness of the proposed method to accelerate the evolution in the time domain using estimated evaluation