Titlebook: Benchmarks and Hybrid Algorithms in Optimization and Applications; Xin-She Yang Book 2023 The Editor(s) (if applicable) and The Author(s),

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2524-552X implementations and coding aspects of benchmarks.Serves as a.This book is specially focused on the latest developments and findings on hybrid algorithms and benchmarks in optimization and their applications in sciences, engineering, and industries. The book also provides some comprehensive reviews a

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Nicolas J. Mueller,Jay A. Fishmang is also important in evaluating the performance of optimization algorithms. This chapter focuses on the overview of optimization, nature-inspired algorithms, and the role of hybridization. We will also highlight some issues with hybridization of algorithms.

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,Nature-Inspired Algorithms in Optimization: Introduction, Hybridization, and Insights,g is also important in evaluating the performance of optimization algorithms. This chapter focuses on the overview of optimization, nature-inspired algorithms, and the role of hybridization. We will also highlight some issues with hybridization of algorithms.

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,Review of Parameter Tuning Methods for Nature-Inspired Algorithms, and can be sufficiently robust for solving different types of optimization problems. This chapter reviews some of the main methods for parameter tuning and then highlights the important issues concerning the latest development in parameter tuning. A few open problems are also discussed with some recommendations for future research.

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,QOPTLib: A Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems,l solving of . using two solvers based on quantum annealing. Our main intention with this is to establish a preliminary baseline, hoping to inspire other researchers to beat these outcomes with newly proposed quantum-based algorithms.

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Metaheuristics for Feature Selection: A Comprehensive Comparison Using Opytimizer,nes based on metaheuristic optimization techniques. This chapter provides a comprehensive comparison among metaheuristic-based architectures for feature selection, as well as a hands-on tutorial followed by a case study using the Opytimizer(.) framework and the Naïve Bayes classifier.

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2524-552X nd surveys on implementations and coding aspects of benchmarks. The book is useful for Ph.D. students and researchers with a wide experience in the subject areas and also good reference for practitioners from academia and industrial applications..978-981-99-3972-5978-981-99-3970-1Series ISSN 2524-552X Series E-ISSN 2524-5538