Abstract:Evolutionary algorithms are inspired by Darwinian evolution by mimicking the mechanisms of natural selection. The most well-known type, namely genetic algorithms (GAs), uses populations of potential solutions represented as chromosomes, subjecting them to selection, crossover, and mutation operations. Tailored for specific problems and characteristics, they tend to be today's much murmured research. This chapter proposes the different EAs and their systematic workflow. EA, the process, begins with the initiali… Show more
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