Abstract
Evolutionary algorithms (EAs) are a family of heuristic search methods that are often used nowadays to find satisfactory solutions to difficult optimization and machine learning problems. EAs are loosely based on a few fundamental evolutionary ideas introduced by Darwin in the nineteenth century. These concepts revolve around the notion of populations of organisms adapting to their environment through genetic inheritance and survival of the fittest. Innovation is provided by various biological recombination and mutation mechanisms. EAs make use of a metaphor whereby an optimization problem takes the place of the environment; feasible solutions are viewed as individuals living in that environment and an individual’s degree of adaptation to its surrounding environment is the counterpart of the objective function evaluated on a feasible solution. In the same way, a set of feasible solutions takes the place of a population of organisms.
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Tomassini, M. (2010). Cellular Evolutionary Algorithms. In: Kroc, J., Sloot, P., Hoekstra, A. (eds) Simulating Complex Systems by Cellular Automata. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12203-3_8
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