Empirical Analysis and Random Respectful Recombination of Crossover and Mutation in Genetic Algorithms
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This paper studies the problem of how changes in the four GA parameters (population size, number of generations, crossover & mutation probabilities) have an effect on GA’s performance from a practical stand point and tests the robustness of GA to control parameters.Abstract:
Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA depend upon the values given to these parameters is a matter of research interest. This paper studies the problem of how changes in the four GA parameters (population size, number of generations, crossover & mutation probabilities) have an effect on GA’s performance from a practical stand point. To examine the robustness of GA to control parameters, we have tested two groups of parameters & the interaction inside the group (a) Crossover & mutation alone (b) Crossover combined with mutation . Based on calculations and simulation results it is seen that for simple problems mutation plays an momentous role. For complex problems crossover is the key search operator. Based on our study complementary crossover & mutation probabilities is a reliable approach.read more
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References
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Book ChapterDOI
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
TL;DR: It is shown empirically that disruption analysis alone is not sufficient for selecting appropriate forms of crossover, but by taking into account the interacting effects of population size and crossover, a general picture begins to emerge.
Proceedings Article
A parameter-less genetic algorithm
TL;DR: This paper explores the development of a GA that fulfills this requirement, and takes into account several aspects of the theory of GAs, including previous research work on population sizing, the schema theorem, building block mixing, and genetic drift.
Forma Analysis and Random Respectful Recombination.
TL;DR: Intrinsic parallelism is shown to have application beyond schemata and o-schemata and more general objects called formae are introduced and general operators which manipulate these are introduced.
Book ChapterDOI
An Analysis of Multi-Point Crossover
TL;DR: This analysis extends the work from De Jong's thesis, which dealt with disruption of n-point crossover on 2nd order hyperplanes, to present various extensions to this theory, including an analysis of the disruption of kth order hyperplane crossover.
Journal Article
Adapting Crossover in Evolutionary Algorithms.
TL;DR: An adaptive mechanism for controlling the use of crossover in an EA is described and an improvement to the adaptive mechanism is presented, which can also be used to enhance performance in a non-adaptive EA.