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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)
The problem of inverting trained feedforward neural networks is to find the inputs which yield a given output. In general, this problem is an ill-posed problem because the mapping from the output space to the input space is a one-to-many mapping. In this paper, we present a method for dealing with the inverse problem by using mathematical programming(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)