Generic Local Search (Memetic) Algorithm on a Single GPGPU Chip

Memetic algorithms (MAs), evolutionary algorithms coupled with a local search routine, have been shown to be very efficient in solving a great variety of problems. This chapter presents the first implementation of a generic parallel MA on a general-purpose graphics processing unit card. An upgrade of the EASEA platform provides an automatic generation and parallelization of an MA for both novice and experienced users. Experiments on a benchmark function and a real-world problem reveal speedups ranging between × 70 and × 120, depending on population size and number of local search iterations.

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