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, and differential evolution, have been developed and studied under this scenario, the performance of an algorithm may vary significantly from problem to problem. This implies that there is an inherent risk associated with the selection of algorithms. We propose that, instead of choosing an existing algorithm and investing the entire time budget in it, it would be less risky to distribute the time among multiple different algorithms. A new approach named population-based algorithm portfolio (PAP), which takes multiple algorithms as its constituent algorithms, is proposed based upon this idea. PAP runs each constituent algorithm with a part of the given time budget and encourages interaction am...

• Type: Journal
• Year: 2010
• Where: TEC
• Authors: Fei Peng, Ke Tang, Guoliang Chen, Xin Yao
• Keywords: Algorithms, Constituent Algorithms, Population-based Algorithm, TEC 2010