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| Content Provider | ACM Digital Library |
|---|---|
| Author | Yuen, Shiu Yin Zhang, Xin Chow, Chi Kin |
| Abstract | 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. |
| Starting Page | 567 |
| Ending Page | 574 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450319638 |
| DOI | 10.1145/2463372.2463435 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-07-06 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Global optimization Evolutionary algorithm Portfolio |
| Content Type | Text |
| Resource Type | Article |
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