No abstract

A large number of algorithms has been proposed for solving continuous optimisation problems. However, there is limited theoretical understanding of the strengths and weaknesses of most algorithms and their individual applicability. Furthermore, the performance of these algorithms is highly dependent on their control parameters, which need to be configured to achieve a peak performance. Automating the processes of selecting the most suitable algorithm and the right control parameters can help in solving continuous optimisation problems effectively and efficiently. In this paper, a simple online algorithm selector is proposed. It decides on selecting the right algorithm based on the current state of the search process to solve a given problem. Each algorithm in the portfolio of the algorithm selector competes with others and utilises the results of other algorithms to locate the global optimum. The proposed algorithm selector and the algorithms of the portfolio as stand-alone algorithms were benchmarked on the noise-free BBOB-2009 testbed. The results show that the performance of the simple algorithm selector is better than the performances of the individual algorithms in general. It was also able to solve eleven out of twenty-four functions of the test suite to the ultimate accuracy of 10-8.