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Evolutionary computation is a sub-field of the metaheuristic methods. Sophisticated Optimization for Spreadsheets. Genetic Algorithms and Grouping Problems. As such, they are aligned with the Building Block Hypothesis in adaptively reducing disruptive recombination. These less fit solutions ensure genetic diversity within the genetic pool of the parents and therefore ensure the genetic diversity of the subsequent generation of children. Annals of Operations Research. As expected, it is also noticeable that the GA with integer restrictions achieves the optimal solution far quicker than the continuous cases.

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GA are most appropriate for complex non-linear models where location of the global optimum is a difficult task. The mutation procedure randomly modifies the genes of an individual subject to a small mutation factor, introducing further randomness into the population. It should be noted that for the GAMS model, a starting point of the origin i. The fitness function is always problem dependent. Problems which appear to be particularly appropriate for solution by genetic algorithms include timetabling and scheduling problems, and many scheduling software packages are based on GAs [ citation needed ].

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