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1 I n t r o d u c t i o n The Genetic Algorithm (GA) is a search and optimization technique based on the mechanism of natural evolution[i, 2]. GA consists of the selection, the crossover, and usually the mutation operators. In the selection operation, an individual having larger fitness value is allowed to yield more offsprings in the next generation. While(More)
This paper discusses the self-adaptive mechanisms of evolution strategies (ES) and real-coded genetic algorithms (RCGA) for optimization in continuous search spaces. For multi-membered evolution strategies, a self-adaptive mechanism of mutation parameters has been proposed by Schwefel. It introduces parameters such as standard deviations of the normal(More)
This paper discusses optimization of functions with uncertainty by means of Genetic Algorithms (GAs). In practical application of such GAs, possible number of fitness evaluation is quite limited. The authors have proposed a GA utilizing history of search (Memory-based Fitness Evaluation GA: MFEGA) so as to reduce the number of fitness evaluation for such(More)
For Real-coded Genetic Algorithms, there have been proposed many crossover operators. The blend crossover (BLX-α) proposed by Eshelman and Schaffer shows good search ability for separable fitness functions. However, because of its component-wise operation, the BLX-α faces difficulties in optimization of non-separable fitness functions. The present paper(More)
Real-coded genetic algorithms (RCGAs) attract attention as global optimization methods for nonlinear functions. For RCGAs, there have been proposed many crossover operators so far. Among them, the unimodal normal distribution crossover (UNDX) developed by Ono et al. shows good performance in optimization of multi-modal and highly non-separable fitness(More)