Fitness evaluation for structural optimisation genetic algorithms using neural networks

This paper relates to the optimisation of structural design using Genetic Algorithms (GAs) and presents an improved method for determining the fitness of genetic codes that represent possible design solutions by using a neural network to generalize fitness. Two problems that often impede design optimization using genetic algorithms are expensive fitness evaluation and high epistasis. In this paper we show that by using a neural network as a fitness approximator, optimal solutions to certain design problems can be achieved in significantly less generations and with considerably less fitness evaluations.