| Summary: | In this work two instances of the examination timetabling problem are studied and solved using memetic algorithms. The first is the uncapacitated single-epoch problem instance. In the second problem instance two examination epochs are considered, with different durations. The memetic algorithm, named Shuffled Complex Evolution Algorithm, uses a population organized into sets called complexes which evolve independently using a recombination and local search operators. Population diversity is preserved by means of the recombination operator and a special solution update mechanism. Experimental evaluation was carried out on the public uncapacitated Toronto benchmarks (single epoch) and on the ISEL-DEETC department examination benchmark (two epochs). Results show that the algorithm is competitive on the Toronto benchmarks, attaining a new lower bound on one benchmark. In the ISEL-DEETC benchmark, the algorithm attains a lower cost when compared with the manual solution.
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