Particle Swarm Optimization with Interval-valued Genotypes and Its Application to Neuroevolution
Authors: Hidehiko Okada
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1335758Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1414
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