A Sequential Learnable Evolutionary Algorithm with a Novel Knowledge Base Generation Method
Sequential learnable evolutionary algorithm (SLEA) provides an algorithm selection framework for solving the black box continuous design optimization problems. An algorithm pool consists of set of established algorithms. A knowledge base is trained offline. SLEA uses the algorithm-problem features to select the best algorithm from the algorithm pool. Given a problem, the default algorithm is run for the initial round. After that, an algorithm-problem feature is collected and used to map to the most similar problem in the knowledge base. Then the best algorithm for solving the problem is used in the second round. This process iterates until \( n \) rounds have been made. It is revealed that the algorithm-problem feature is a good problem identifier, thus SLEA performs well on the known problems that have been encountered. However, the performance on those unknown problems is limited if the knowledge base is biased. In this paper, we propose a modified SLEA, which performs the training process using a novel method. A relatively unbiased knowledge base is formed. Experimental results show that the modified SLEA maintains the performance of SLEA on solving the CEC 2013 test suite, while it performs better than SLEA on solving a set of randomly generated max-set of Gaussian test problems.
KeywordsEvolutionary algorithm Algorithm selection Black box design optimization problem Algorithm-problem feature
The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 125313]. Yang Lou acknowledge the Institutional Postgraduate Studentship and the Institutional Research Tuition Grant from City University of Hong Kong.
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