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The author proposes an extension of particle swarm optimization (PSO) for solving interval-valued optimization problems and applies the extended PSO to evolutionary training of neural networks (NNs) with interval weights. In the proposed PSO, values in the genotypes are not real numbers but intervals. Experimental results show that interval-valued NNs trained by the proposed method could well approximate hidden target functions despite the fact that no training data was explicitly provided. Keywords—Evolutionary algorithms, swarm intelligence, particle swarm optimization, neural network, interval arithmetic.

Particle Swarm Optimization with Interval-valued Genotypes and Its Application to Neuroevolution