Evolutionary Computation

Part of the Power Systems book series (POWSYS)


The basic approach to optimisation is to formulate a fitness function, which evaluates the performance of the fitness function and improves this performance by choosing from available alternatives. Most classical optimisation methods produce a deterministic sequence of trial solutions using the gradient or higher-order statics of the fitness function. However, such methods may converge to local optimal solutions. The evolutionary computation approach is a population-based optimisation process rooted on the model of organic evolution, which can outperform the classical optimisation methods for many engineering problems. The existing approaches to evolutionary computation include genetic algorithms, evolution strategies, evolutionary programming, genetic programming and so on, which are considerably different in their practical instantiations. The emphasis of this chapter is put on the biological background and basic foundations of genetic algorithm and evolutionary programming. As the principles of particle swarm optimisation are similar to that of evolutionary algorithms, the standard particle swarm optimisation algorithm and an improved particle swarm optimisation algorithm are also presented in this chapter.


Particle Swarm Optimisation Fitness Function Inertia Weight Simple Genetic Algorithm Standard Particle Swarm Optimiser 
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© Springer-Verlag London Limited  2011

Authors and Affiliations

  1. 1.Department of Electrical Engineering and ElectronicsThe University of LiverpoolBrownlow HillUK

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