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Adaptation in natural and artificial systems
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Genetic Algorithms in Search, Optimization and Machine Learning
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The Design of Innovation: Lessons from and for Competent Genetic Algorithms
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Proceedings of the 3rd International Conference on Genetic Algorithms
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Parallel Genetic Algorithms Population Genetics and Combinatorial Optimization
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Optimization Using Distributed Genetic Algorithms
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A Comparative Study of Five Parallel Genetic Algorithms using the Traveling Salesman Problem
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Parallel genetic algorithms (PGAs) have been developed to reduce the execution time of serial genetic algorithms (SGA) and to solve larger problems. They typically find better solutions, as they are likely to be more resistant to premature convergence to local minima compared to serial genetic algorithms. This paper presents a comparative study of four different PGAs using the traveling salesman problem (TSP) as the case application in order to quantify their performance on the basis of small initial populations. Besides the well-known parallelization approaches, a new parallelization scheme is considered which combines iterative data exchanges and new solutions generation, thus extending a search space during the evolution. To make the comparison fair, all PGAs are using the same baseline serial genetic algorithm, started from the same set of initial populations and tested on the same known instances from the TSPLIB-archive.