Evolutionary computation implementations

This chapter discusses the common issues related to the implementation of evolutionary algorithms, focusing on two implementations of evolutionary computation: a genetic algorithm (GA) implementation and a particle swarm optimization (PSO) implementation. A particle swarm optimizer can be used both for the optimization of nonlinear functions and for optimization problems that require multi–particle swarm optimizers running simultaneously. For the representation of multivalue discrete parameters, a more natural and intuitive way is to use integer representation. Also, binary representation can be easily transformed into integer representation. To overcome the inaccuracy problems introduced by using binary representations for encoding real values, a more natural and intuitive way is to use real-valued representations to encode real value parameters. The use of real-valued representations makes it possible to use large domains for the variables, which is difficult to achieve with binary and integer representations.