 Introduction of Biogeography-Based Programming as a new algorithm for solving problemsEmadaldin Mohammadi Golafshani Applied Mathematics and Computation, 2015, vol. 270, issue C, 1-12 Abstract: Application of evolutionary computation techniques is relatively novel for machine learning. Motivated by different types of evolutionary computation techniques, different types of automatic programming were proposed. Biogeography-Based Optimization (BBO) is a new evolutionary algorithm that is inspired by the science of biogeography and has been shown to be competitive to other population-based algorithms. Inspired by biogeography theory and previous results, in this paper Biogeography-Based Programming (BBP) is proposed as a new type of automatic programming for creating polynomial regression models. In order to show the effectiveness of the proposed BBP, a number of experiments were carried out on a suite set of benchmark functions and the results were also compared with several existing automatic programming algorithms. Furthermore, sensitivity analysis was performed for the parameter settings of the proposed BBP. The results indicate that the proposed model is promising in terms of success rate and accuracy and it performs better than other algorithms investigated in this consideration. Keywords: Biogeography-Based Programming; Machine learning; Automatic programming (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:270:y:2015:i:c:p:1-12 DOI: 10.1016/j.amc.2015.08.026 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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