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A migration strategy for distributed evolutionary algorithms based on stopping non-promising subpopulations: A case study on routing problems

Eneko Osaba, Enrique Onieva, Fernando Dia, Roberto Carballedo, Pedro Lopez, Asier Perallos

Abstract


Nowadays, parallel genetic algorithms are one of the most used meta-heuristics for solving combinatorial optimization problems. One of the challenges that arise when implementing these kind of algorithms is the communication between subpopulations. This communication, called migration, is a determining factor for a good performance of the algorithm. In this short note, a new approach for the subpopulations communication is presented. This new strategy is called Standstill and Parade. The basis of this new strategy is to stop non-promising subpopulations, in order to focus the search on those that demonstrate more effectiveness. To prove the quality of this approach seven different migration functions are compared. For the experimentation, two different routing problems have been used.

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