Population-Based Algorithm Portfolios for Numerical Optimization

In this paper, we consider the scenario that a population-based algorithm is applied to a numerical optimization problem and a solution needs to be presented within a given time budget. Although a wide range of population-based algorithms, such as evolutionary algorithms, particle swarm optimizers,...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 14; H. 5; S. 782 - 800
Hauptverfasser:	Peng, Fei, Tang, Ke, Chen, Guoliang, Yao, Xin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.10.2010 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithm portfolios Algorithmics. Computability. Computer arithmetics Algorithms Application software Applied sciences Artificial intelligence Budgeting Budgets Computer applications Computer science Computer science; control theory; systems Constituents Councils Education Evolution Evolutionary algorithms Evolutionary computation Exact sciences and technology global optimization Laboratories Mathematical models metaheuristic algorithms Migration numerical optimization Optimization Particle swarm optimization population-based algorithms Portfolios Risk Studies Theoretical computing Algorithm portfolios Script Probabilistic approach Evolutionary algorithm Algorithmics Global optimum numerical optimization Optimization global optimization Credit Heuristic method Population Budget Population dynamics Swarm intelligence Metric Portfolio management Metamodel metaheuristic algorithms population-based algorithms Mathematical programming Execution time
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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