Yazılım Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/2147
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Article Citation - WoS: 38Citation - Scopus: 44Hyper-Heuristics: a Survey and Taxonomy(Pergamon-elsevier Science Ltd, 2024) Kucukyilmaz, Tayfun; Talbi, El-Ghazali; Dokeroglu, TanselHyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-heuristics to solve challenging optimization problems. They differ from traditional (meta)-heuristics methods, which primarily employ search space-based optimization strategies. Due to the remarkable performance of hyper-heuristics in multi-objective and machine learning-based optimization, there has been an increasing interest in this field. With a fresh perspective, our work extends the current taxonomy and presents an overview of the most significant hyper-heuristic studies of the last two decades. Four categories under which we analyze hyperheuristics are selection hyper-heuristics (including machine learning techniques), low-level heuristics, target optimization problems, and parallel hyper-heuristics. Future research prospects, trends, and prospective fields of study are also explored.Article Citation - WoS: 238Citation - Scopus: 308A Comprehensive Survey on Recent Metaheuristics for Feature Selection(Elsevier, 2022) Dokeroglu, Tansel; Deniz, Ayca; Kiziloz, Hakan EzgiFeature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well.