Advanced methods for evolutionary optimisation

Abstract

In this paper we present two advanced methods for evolutionary optimisation. One method is based on Parallel Genetic Algorithms. It is called Co-operating Populations with Different Evolution Behaviours (CoPDEB), and allows each population to exhibit a different evolution behaviour. Results from two problems show the advantage of using different evolution behaviour on each population. The other method concerns application of GAs on constrained optimisation problems. It is called the Varying Fitness Function (VFF) method and implements a fitness function with varying penalty terms, added to the objective function for penalising infeasible solutions, in order to assist the GA to easily locate the area of the global optimum. Simulation results on two real world problems show that the VFF method outperforms the classic static fitness function implementations. 1. Introduction Using a serial Genetic Algorithm with a static quality function is a wise decision in a great number of optimisatio...