Incorporation family competition into Gaussian and Cauchy mutations to training neural networks using an evolutionary algorithm

The paper presents an evolutionary technique to train neural networks in tasks requiring learning behavior based on family competition principles and adaptive rules that performs better than other canonical evolutionary algorithms, such as genetic algorithms, evolution strategies, and evolutionary programming.

Abstract

The paper presents an evolutionary technique to train neural networks in tasks requiring learning behavior. Based on family competition principles and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations. Different mutations act global and local strategies separately to balance the trade-off between solution quality and convergence speed. The algorithm proposed herein is applied to two different task domains: Boolean functions and artificial ant problem. Experimental results indicate that in all tested problems, the proposed algorithm performs better than other canonical evolutionary algorithms, such as genetic algorithms, evolution strategies, and evolutionary programming. Moreover, essential components such as mutation operators and adaptive rules in the proposed algorithm are thoroughly analyzed.