Abstract
The constraint satisfaction problem is a useful and well-studied framework for the modeling of many problems rising in Artificial Intelligence and other areas of Computer Science. As many real-world optimization problems become increasingly complex and hard to solve, better optimization algorithms are always needed. Genetic algorithms (GA) which belongs to the class of evolutionary algorithms is regarded as a highly successful algorithm when applied to a broad range of discrete as well continuous optimization problems. This paper introduces a hybrid approach combining a genetic algorithm with the multilevel paradigm for solving the maximum constraint satisfaction problem. The promising performances achieved by the proposed approach is demonstrated by comparisons made to solve conventional random benchmark problems.
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