Abstract
Genetic algorithms (GAs) are relatively new combinatorial search methods which have been used in the solution optimization problems, machine learning and general search problems in numerous fields [Goldberg, 1989; Holland, 1975, Davis, 1991]. In GAs the problem analyzed is conceptualized as a living environment and the computational process is formulated as an iterative-evolutionary process with similarities to evolution of biological systems. GAs may also be identified as iterative stochastic search processes based on the methods employed in the computational steps. In this algorithm, first a random initial population is generated and coded. Based on certain characteristics of this population, a new population is generated by means of three primary operations identified as “selection,” “crossover (mating)” and “mutation.” These three operations, in essence, simulate the mechanisms of natural selection and evolution. In these computations each member of the population, at every stage of the evolution, is a solution to the problem being analyzed. The goal in this evolutionary process is for the new population to have a higher “quality” than the previous one. In optimization problems the “quality” of a member of a population may be measured in terms of the value of the objective function. That is, every population will have a different objective function value and there are better populations which yield a maximum (minimum) value for the objective function. The iterative process of generation of new populations continues until the population converges on a suitable maximum or minimum value of the objective function evaluated. Once this is achieved the optimal solution of the problem is considered solved. Computational steps of this process will be briefly presented below.
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© 1996 Kluwer Academic Publishers
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Aral, M.M., Guan, J. (1996). Genetic Algorithms in Search of Groundwater Pollution Sources. In: Aral, M.M. (eds) Advances in Groundwater Pollution Control and Remediation. NATO ASI Series, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0205-3_17
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DOI: https://doi.org/10.1007/978-94-009-0205-3_17
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