Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Journal of Global Optimization
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Self-adaptive multimethod search for global optimization in real-parameter spaces
IEEE Transactions on Evolutionary Computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Population-based algorithm portfolios for numerical optimization
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Algorithm portfolio selection as a bandit problem with unbounded losses
Annals of Mathematics and Artificial Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
IEEE Transactions on Evolutionary Computation
On composing an (evolutionary) algorithm portfolio
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Many good evolutionary algorithms have been proposed in the past. However, frequently, the question arises that given a problem, one is at a loss of which algorithm to choose. In this paper, we propose a novel algorithm portfolio approach to address the above problem. A portfolio of evolutionary algorithms is first formed. Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Composite DE (CoDE), Particle Swarm Optimization (PSO2011) and Self adaptive Differential Evolution (SaDE) are chosen as component algorithms. Each algorithm runs independently with no information exchange. At any point in time, the algorithm with the best predicted performance is run for one generation, after which the performance is predicted again. The best algorithm runs for the next generation, and the process goes on. In this way, algorithms switch automatically as a function of the computational budget. This novel algorithm is named Multiple Evolutionary Algorithm (MultiEA). Experimental results on the full set of 25 CEC2005 benchmark functions show that MultiEA outperforms i) Multialgorithm Genetically Adaptive Method for Single Objective Optimization (AMALGAM-SO); ii) Population-based Algorithm Portfolio (PAP); and iii) a multiple algorithm approach which chooses an algorithm randomly (RandEA). The properties of the prediction measures are also studied. The portfolio approach proposed is generic. It can be applied to portfolios composed of non-evolutionary algorithms as well.