Performance of genetic algorithms and simulated annealing in the economic optimization of a herd dynamics model |
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| Institution: | 1. Section of Medical and Forensic Anthropology (UVSQ & EA4569), UFR of Health Sciences, 2 Avenue de la Source de la Bièvre, 78180 Montigny-Le-Bretonneux, France;2. CASH & IPES, 403 Avenue de la République, 92000 Nanterre, France;3. Information Génomique & Structurale (UMR7256), Institut de Microbiologie de la Méditerranée, Aix-Marseille University & CNRS, Marseille, France;4. Assistance Publique des Hôpitaux de Marseille (APHM), Marseille, France;5. Chaire de Microbiologie, Collège de France, Place Marcelin Berthelot, 75005 Paris, France;6. Institut Pasteur, rue Vaugirard, 75014 Paris, France;7. Chaire de Paléo-anthropologie, Collège de France, Place Marcelin Berthelot, 75005 Paris, France |
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| Abstract: | This study focuses on replicated exploratory optimizations of a large and difficult beef herd dynamics model, using the net present value over a 10-year planning horizon as the variable of interest. Faced with a practical search-space of the order of 10100 possible management decision combinations, the thorough but slow search pattern of simulated annealing struggled, on average falling 1.2% short of the global optimum of the system. By comparison, the cross-breeding and mutating nature of the genetic algorithm searches usually produced good results, averaging 0.1% from the global optimum. Also, these were achieved with about half the computing time used by the simulated annealing optimizations. Hence, for this problem, genetic algorithms proved the superior method. |
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