Accelerated evolutionary computation using fitness estimation

Evolutionary computation (EC) is widely applied to various kinds of combinatorial optimization problems. EC is generally a time-consuming paradigm because it needs 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 early generations. In spite of these improvements, ECs still require much time to obtain the solution in many practical engineering applications. Fitness evaluation usually spends most of the 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, the time saving in the evaluation scheme exceeds the deceleration. As a result, we can obtain suboptimal solutions relatively faster. The simulation results of the designed fuzzy logic controller using GA show the effectiveness of the proposed method to accelerate evolution in the time domain. The simulation results applied to De Jong's (1975) Test Function show the applicability to various problems of the proposed method.