Using Quasi Random Sequences in Genetic Algorithms

Heikki Maaranen, Kaisa Miettinen and Marko M. Mäkelä
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Heikki Maaranen: University of Jyväskylä, Department of Mathematical Information Technology
Kaisa Miettinen: University of Jyväskylä, Department of Mathematical Information Technology
Marko M. Mäkelä: University of Jyväskylä, Department of Mathematical Information Technology

A chapter in Optimization and Inverse Problems in Electromagnetism, 2003, pp 33-44 from Springer

Abstract: Abstract The selection of initial points in a population-based heuristic optimization method is important since it affects the search for several iterations and often has an influence on the final solution. If no a priori information about the optimization problem is available, the initial population is often selected randomly using pseudo random numbers. Many times, however, it is more important that the points are as evenly distributed as possible than that they imitate random points. Therefore, we have studied the use of quasi random sequences in the initialization of a genetic algorithm. Sample points in a quasi random sequence are designed to have very good distribution properties. The modified genetic algorithms using quasi random sequences in the initial population have been tested by solving a large number of continuous benchmark problems from the literature. The numerical results of three genetic algorithm implementations using different quasi random sequences have been compared to those of a traditional implementation of using pseudo random numbers. The results are promising.

Keywords: random numbers; global continuous optimization; genetic algorithms (search for similar items in EconPapers)
Date: 2003
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-017-2494-4_4

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DOI: 10.1007/978-94-017-2494-4_4

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