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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)
In this paper we present a parallel and modular multi-sieving neural network (PMSN) architecture for constructive learning. This PMSN architecture is dierent from existing constructive learning networks such as the cascade correlation architecture. The constructing element of the PMSNs is a compound modular network rather than a hidden unit. This compound(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)