A Neuro-Genetic Technique for Pruning and Optimization of ANN Weights

Sakshi Sakshi, Ravi Kumar

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

A novel technique for optimization of artificial neural network (ANN) weights which combines pruning and Genetic Algorithm (GA) has been proposed. The technique first defines “relevance” of initialized weights in a statistical sense by introducing a coefficient of dominance for each weight and subsequently employing the concept of complexity penalty. Based upon complexity penalty for each weight, candidate solutions are initialized to participate in the Genetic optimization. The GA stage employs mean square error as the fitness function which is evaluated once for all candidate solutions by running the forward pass of backpropagation. Subsequent reproduction cycles generate fitter individuals and the GA is terminated after a small number of cycles. It has been observed that ANNs trained with GA optimized weights exhibit higher convergence, lower execution time, and higher success rate in the test phase. Furthermore, the proposed technique yields substantial reduction in computational resources.

Original languageEnglish
Pages (from-to)1-26
Number of pages26
JournalApplied Artificial Intelligence
Volume33
Issue number1
DOIs
StatePublished - 2 Jan 2019
Externally publishedYes

ASJC Scopus subject areas

  • Artificial Intelligence

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