Displaying similar documents to “Advances in parallel heterogeneous genetic algorithms for continuous optimization”

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...

Solving the simple plant location problem by genetic algorithm

Jozef Kratica, Dušan Tošic, Vladimir Filipović, Ivana Ljubić (2001)

RAIRO - Operations Research - Recherche Opérationnelle

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The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solve this problem. By using the developed algorithm it is possible to solve SPLP with more than 1000 facility sites and customers. Computational results are presented and compared to dual based algorithms.