Yazarlar
Yayınlar
- MakaleYalnız künye2012Scopus SJRQ1A novel chemistry based metaheuristic optimization method for mining of classification rules
Alatas, Bilal
When investigated carefully, chemical reactions possess efficient objects, states, process, and events that can be designed as a computational method en bloc. In this study, a novel computational method, which is robust and have less parameters than that of used in the literature, is intended to be developed inspiring from types and occurring of chemical reactions. The proposed method is named as artificial chemical reaction optimization algorithm, ACROA. In this study, one of the first applications of this method has been performed in classification rule discovery field of data mining and efficiency has been demonstrated. (C) 2012 Elsevier Ltd. All rights reserved.
AtıfWoS59OpenAlex75
İncele - MakaleYalnız künye2011Scopus SJRQ1Uniform Big Bang-Chaotic Big Crunch optimization
Alatas, Bilal
This study proposes methods to improve the convergence of the novel optimization method, Big Bang-Big Crunch (BB-BC). Uniform population method has been used to generate uniformly distributed random points in the Big Bang phase. Chaos has been utilized to rapidly shrink those points to a single representative point via a center of mass in the Big Crunch phase. The proposed algorithm has been named as Uniform Big Bang-Chaotic Big Crunch (UBB-CBC). The performance of the UBB-CBC optimization algorithm demonstrates superiority over the BB-BC optimization for the benchmark functions. (C) 2010 Elsevier B.V. All rights reserved.
AtıfWoS48OpenAlex62İncele