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Best Practices in Reporting Results with Parallel Genetic Algorithms

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Parallel Genetic Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 367))

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Abstract

Most optimization tasks found in real world applications impose several constraints that frequently prevent the utilization of exact methods. The complexity of these problems (they are often NP-hard [78]) or the limited computational resources available to solve them (time, memory) have made the development of metaheuristics a major field in present research. In these cases, metaheuristics provide optimal or suboptimal feasible solutions in a reasonable time. Although the use of metaheuristics allows to significantly reduce the time of the search process, the high dimension of many tasks will always pose problems and result in time-consuming scenarios for industrial problems. Therefore, parallelism is an approach not only to reduce the resolution time, but also to improve the quality of the provided solutions. This last holds since parallel algorithms usually run a different search model with respect to sequential ones [35] (see also Chapter 1).

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© 2011 Springer-Verlag Berlin Heidelberg

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Luque, G., Alba, E. (2011). Best Practices in Reporting Results with Parallel Genetic Algorithms. In: Parallel Genetic Algorithms. Studies in Computational Intelligence, vol 367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22084-5_3

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  • DOI: https://doi.org/10.1007/978-3-642-22084-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22083-8

  • Online ISBN: 978-3-642-22084-5

  • eBook Packages: EngineeringEngineering (R0)

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