GAs: Selected Topics
Abstract

the coding of the problem often moves the GA to operate in a different space than that of the problem itself,

there is a limit on the hypothetically unlimited number of iterations, and

there is a limit on the hypothetically unlimited population size.
Eshelman and Schaffer [105] discuss a few strategies for combating premature convergence; these include (1) a mating strategy, called incest prevention,^{1} (2) a use of uniform crossover (see section 4.6), and (3) detecting duplicate strings in the population (similar to the crowding model; see section 4.1).“...While the performance of most implementations is comparable to or better than the performance of many other search techniques, it [GA] still fails to live up to the high expectations engendered by the theory. The problem is that, while the theory points to sampling rates and search behavior in the limit, any implementation uses a finite population or set of sample points. Estimates based on finite samples inevitably have a sampling error and lead to search trajectories much different from those theoretically predicted. This problem is manifested in practice as a premature loss of diversity in the population with the search converging to a suboptimal solution.”
Keywords
Genetic Algorithm Penalty Function Knapsack Problem Genetic Operator Premature ConvergencePreview
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