Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Efficient parallel LAN/WAN algorithms for optimization: the MALLBA project
Parallel Computing
Cellular Genetic Algorithms
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Solving very large instances of the scheduling of independent tasks problem on the GPU
Journal of Parallel and Distributed Computing
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Cellular genetic algoritms (cGAs) are characterized by its grid structure population, in which individuals can only interact with their neighbors. This kind of algorithms has demonstrated to have a high numerical performance thanks to the good exploration/exploitation balance they perform in the search space. Although cGAs seem very appropriate for parallelism, there is a low number of works proposing or studing parallel models for clusters of computers. This is probably because the model requires a high communication level between sub-populations due to the tight interactions among individuals. These parallel versions are however needed to cope with the high computational requirements of the current real-world problems. This article proposes a new parallel cellular genetic algorithm which maintains (or even improves because its asynchronicity) the numerical behaviour of a serial cGA, while at the same time it provokes an important reduction on the execution time for finding the optimal solution.