Titlebook: Metaheuristics for Machine Learning; New Advances and Too Mansour Eddaly,Bassem Jarboui,Patrick Siarry Book 2023 The Editor(s) (if applicab

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Michael Heider,David P?tzel,Helena Stegherr,J?rg H?hnerf handling examination questions that is clearly superior to the rather . treatment proposed in ?qvist (1969), and (3) to a useful application to questions in police interrogations for the purpose of eliciting confessions—a topic that seems so far to have been fairly neglected by interrogative theor

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From Metaheuristics to Automatic Programmingcontributions in this area trying to form a starting point for other researchers. Our objective is to stimulate the interest of the research community and keeping them updated on automatic programming using metaheuristics approaches and hoping to motivate the appearance of innovated contributions.

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Biclustering Algorithms Based on Metaheuristics: A Reviewods and their main search components: representation, objective function, and variation operators. A specific discussion on single versus multi-objective approaches is presented. Finally, some emerging research directions are presented.

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A Metaheuristic Perspective on Learning Classifier Systemso LCSs. Furthermore, we discuss the different problems that metaheuristics are solving when applied in this context, for example, discrete or real valued input domains, optimization of individual rule conditions or entire sets of rule conditions, and fitness functions that support nicheing or contro

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Algorithm vs Processing Manipulation to Scale Genetic Programming to Big Data Miningtends GP with active learning using dynamic and adaptive sampling. For each approach, some guidelines of implementation into the GP loop over an EA Python framework are given. A combination of the two approaches is also presented. The efficiency of these solutions is then discussed according to some

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