Differential Evolution with Reversible Linear Transformations

• URL: http://arxiv.org/abs/2002.02869v1
• Date: Fri, 7 Feb 2020 16:05:54 GMT
• Title: Differential Evolution with Reversible Linear Transformations
• Authors: Jakub M. Tomczak and Ewelina Weglarz-Tomczak and Agoston E. Eiben
• Abstract summary: We propose to generate new candidate solutions by utilizing reversible linear transformation applied to a triplet of solutions from the population. In other words, the population is enlarged by using newly generated individuals without evaluating their fitness.
• Score: 8.873449722727026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differential evolution (DE) is a well-known type of evolutionary algorithms (EA). Similarly to other EA variants it can suffer from small populations and loose diversity too quickly. This paper presents a new approach to mitigate this issue: We propose to generate new candidate solutions by utilizing reversible linear transformation applied to a triplet of solutions from the population. In other words, the population is enlarged by using newly generated individuals without evaluating their fitness. We assess our methods on three problems: (i) benchmark function optimization, (ii) discovering parameter values of the gene repressilator system, (iii) learning neural networks. The empirical results indicate that the proposed approach outperforms vanilla DE and a version of DE with applying differential mutation three times on all testbeds.

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