CoteParrottSabourin2007
Référence
Cote, P., Parrott, L. and Sabourin, R. (2007) Multi-objective optimization of an ecological assembly model. Ecological Informatics, 2:23-31.
Résumé
The aim of the present work is to use multi-objective evolutionary algorithms (MOEA) to parametrise an ecological assembly model based on Lotka-Volterra dy- namics. In community assembly models, species are introduced from a pool of species according to a sequence of invasion. By manipulating the assembly sequences, we look at the structure of the ¯nal communities obtained by a multi-objective process where the goal is to optimize the productivity of the ¯nal communities. The MOEA must also meet the constraint that the communities constructed in this fashion have a speci¯ed connectance. The Non-dominated Sorting Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2) were employed to optimize se- quences according to the multi-objective optimization problem. The results show that the assembly process using optimized sequences generated di®erent community structure than those generated via random sequences. First, the assembled commu- nities are much more productive than those obtained from random sequences. We show that this increase of productivity is due to the degree distribution of the com- munity food web, which was reshaped by the optimization process. In addition, using identical regional species pools the MOEAs were able to generate communi- ties of di®erent expected connectances. These results demonstrate the e®ectiveness of NSGA-II and SPEA2 for optimizing parameters in ecological models.
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@ARTICLE { CoteParrottSabourin2007,
AUTHOR = { Cote, P. and Parrott, L. and Sabourin, R. },
TITLE = { Multi-objective optimization of an ecological assembly model },
JOURNAL = { Ecological Informatics },
YEAR = { 2007 },
VOLUME = { 2 },
PAGES = { 23-31 },
ABSTRACT = { The aim of the present work is to use multi-objective evolutionary algorithms (MOEA) to parametrise an ecological assembly model based on Lotka-Volterra dy- namics. In community assembly models, species are introduced from a pool of species according to a sequence of invasion. By manipulating the assembly sequences, we look at the structure of the ¯nal communities obtained by a multi-objective process where the goal is to optimize the productivity of the ¯nal communities. The MOEA must also meet the constraint that the communities constructed in this fashion have a speci¯ed connectance. The Non-dominated Sorting Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2) were employed to optimize se- quences according to the multi-objective optimization problem. The results show that the assembly process using optimized sequences generated di®erent community structure than those generated via random sequences. First, the assembled commu- nities are much more productive than those obtained from random sequences. We show that this increase of productivity is due to the degree distribution of the com- munity food web, which was reshaped by the optimization process. In addition, using identical regional species pools the MOEAs were able to generate communi- ties of di®erent expected connectances. These results demonstrate the e®ectiveness of NSGA-II and SPEA2 for optimizing parameters in ecological models. },
KEYWORDS = { Assembly Models, Food Webs, Community Assembly, Genetic Algorithms, Multi-Objective Optimization, Lotka-Volterra Dynamics },
OWNER = { brugerolles },
TIMESTAMP = { 2007.12.05 },
}
