131 : Evolutionary Computation

A general introduction to artificial intelligence methods of measurement signal processing is given in Article 128, Nature and Scope of AI Techniques, Volume 2. In nature, evolution is mostly determined by natural selection or different individuals competing for resources in the environment. Those individuals that are better are more likely to survive and propagate their genetic material. The encoding for genetic information (genome) is done in a way that admits asexual reproduction, which results in offspring that are genetically identical to the parent. Sexual reproduction allows some exchange and reordering of chromosomes, producing offspring that contain a combination of information from each parent. This is the recombination operation, which is often referred to as crossover because of the way strands of chromosomes cross over during the exchange. The diversity in the population is achieved by mutation operation. Evolutionary algorithms are ubiquitous nowadays, having been successfully applied to numerous problems from different domains, including optimization, automatic programming, signal processing, bioinformatics, social systems, and so on. In many cases, the mathematical function, which describes the problem, is not known, and the values at certain parameters are obtained from simulations. In contrast to many other optimization techniques, an important advantage of evolutionary algorithms is they can cope with multimodal functions. Usually found grouped under the term evolutionary computation or evolutionary algorithms (Bäck, 1996), are the domains of genetic algorithms (GA) (Holland, 1975), evolution strategies (Rechenberg, 1973; Schwefel, 1977), evolutionary programming (Fogel, Owens and Walsh, 1966), and genetic programming (Koza, 1992). These all share a common conceptual base of simulating the evolution of individual structures via processes of selection, recombination, and mutation reproduction, thereby producing better solutions. The processes depend on the perceived performance of the individual structures as defined by the problem. A population of candidate solutions (for the optimization task to be solved) is initialized. New solutions are created by applying reproduction operators (crossover and/or mutation). The fitness (how good the solutions are) of the resulting solutions is evaluated and suitable selection strategy is then applied to determine which solutions will be maintained into the next generation. The procedure is then iterated, as illustrated in Figure 1.