Genetic algorithms and random keys for sequencing and optimization

Genetic algorithms and random keys for sequencing and optimization

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Article ID: iaor19982236
Country: United States
Volume: 6
Issue: 2
Start Page Number: 154
End Page Number: 160
Publication Date: Mar 1994
Journal: ORSA Journal On Computing
Authors:
Keywords: artificial intelligence, optimization: simulated annealing
Abstract:

In this paper we present a general genetic algorithm to address a wide variety of sequencing and optimization problems including multiple machine scheduling, resource allocation, and the quadratic assignment problem. When addressing such problems, genetic algorithms typically have difficulty maintaining feasibility from parent to offspring. This is overcome with a robust representation technique called random keys. Computational results are shown for multiple machine scheduling, resource allocation, and quadratic assignment problems.

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