A new search model for evolutionary algorithms.
Gog, Anca, Dumitrescu, Dan (2005)
Acta Universitatis Apulensis. Mathematics - Informatics
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Displaying similar documents to “Advances in parallel heterogeneous genetic algorithms for continuous optimization”
A new search model for evolutionary algorithms.
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Adaptive bacterial foraging optimization.
Chen, Hanning, Zhu, Yunlong, Hu, Kunyuan (2011)
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Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem.
Belkadi, K., Gourgand, M., Benyettou, M. (2006)
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Towards a theory of practice in metaheuristics design: A machine learning perspective
Mauro Birattari, Mark Zlochin, Marco Dorigo (2006)
RAIRO - Theoretical Informatics and Applications
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A number of methodological papers published during the last years testify that a need for a thorough revision of the research methodology is felt by the operations research community – see, for example, [Barr (1995) 9–32; Eiben and Jelasity, 582–587; Hooker, (1995) 33–42; Rardin and Uzsoy, (2001) 261–304]. In particular, the performance evaluation of nondeterministic methods, including widely studied metaheuristics such as evolutionary...
Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms
Angelova, Maria, Tzonkov, Stoyan, Pencheva, Tania (2010)
Serdica Journal of Computing
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Fermentation processes as objects of modelling and high-quality control are characterized with interdependence and time-varying of process variables that lead to non-linear models with a very complex structure. This is why the conventional optimization methods cannot lead to a satisfied solution. As an alternative, genetic algorithms, like the stochastic global optimization method, can be applied to overcome these limitations. The application of genetic algorithms is a precondition for...