Genetic algorithms and random keys for sequencing and optimization. (English) Zbl 0807.90060

Summary: 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.


90B35 Deterministic scheduling theory in operations research
90-08 Computational methods for problems pertaining to operations research and mathematical programming
90C27 Combinatorial optimization
92D10 Genetics and epigenetics
68T05 Learning and adaptive systems in artificial intelligence
90B80 Discrete location and assignment
90C20 Quadratic programming
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