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This paper introduces a genetic local search algorithm for bi- nary constraint satisfaction problems. The core of the algo- rithm consists of an ad-hoc optimization procedure followed by the application of blind genetic operators. A standard set of benchmark instances is used in order to assess the performance of the algorithm. The results indicate that this apparently naive hybridation of a genetic algorithm with lo- cal search yields a rather powerful heuristic algorithm for random binary constraint satisfaction problems.

A genetic local search algorithm for random binary constraint satisfaction problems