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Guided Genetic Algorithm and its Application to Radio Link Frequency Assignment Problems

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Abstract

The Guided Genetic Algorithm (GCA) is a hybrid of Genetic Algorithm and Guided Local Search, a meta–heuristic search algorithm. As the search progresses, GGA modifies both the fitness function and fitness template of candidate solutions based on feedback from constraints. The fitness template is then used to bias crossover and mutation. The Radio Link Frequency Assignment Problem (RLFAP) is a class of problem that has practical relevance to both military and civil applications. In this paper, we show how GGA can be applied to the RLFAP. We focus on an abstraction of a real life military application that involves the assigning of frequencies to radio links. GGA was tested on a set of eleven benchmark problems provided by the French military. This set of problems has been studied intensively by a number of prominent groups in Europe. It covers a variety of needs in military applications, including the satisfaction of constraints, finding optimal solutions that satisfy all the constraints and optimization of some objective functions whenever no solution exist (“partial constraint satisfaction”). Not only do these benchmark problems vary in problem nature, they are reasonably large for military applications (up to 916 variables, and up to 5548 constraints). This makes them a serious challenge to the generality, reliability as well as efficiency of algorithms. We show in this paper that GGA is capable of producing excellent results reliably in the whole set of benchmark problems.

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Authors and Affiliations

  1. Dept. of Computer Science, University of Essex, Wivenhoe Park, Colchester, CO4 SSQ, UK

    T. L. Lau & E. P. K. Tsang

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  1. T. L. Lau
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  2. E. P. K. Tsang
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Lau, T.L., Tsang, E.P.K. Guided Genetic Algorithm and its Application to Radio Link Frequency Assignment Problems. Constraints 6, 373–398 (2001). https://doi.org/10.1023/A:1011406425471

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