Contemporary Advancement in Evolutionary Multi-objective Optimization
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Beschreibung
This endeavour presents a rich collection of contemporary advances in evolutionary multi-objective optimisation algorithms, blending foundational concepts with state-of-the-art research and real-world applications. Featuring contributions from leading experts, it offers clear insights into modern EMO methodologies, emerging AI-EC integrations, and diverse problem-solving domains. An essential reference for researchers, practitioners, and students exploring intelligent optimisation Algorithms.
This endeavour presents a rich collection of contemporary advances in evolutionary multi-objective optimisation algorithms, blending foundational concepts with state-of-the-art research and real-world applications. Featuring contributions from leading experts, it offers clear insights into modern EMO methodologies, emerging AI-EC integrations, and diverse problem-solving domains. An essential reference for researchers, practitioners, and students exploring intelligent optimisation Algorithms.
Explores contemporary breakthroughs and emerging trends in multi-objective optimization Showcases practical applications across robotics, bioinformatics, and more, driving impactful solutions Features insights from top international researchers, presenting the latest in MOO advancements
Autor*in
Somnath Mukhopadhyay
Themen in »Contemporary Advancement in Evolutionary Multi-objective Optimization«
Evolutionary multi-objective optimization Machine Learning Pareto-optimal set Pareto optimal front Exploration Exploitation Bioinformatics multi-guide particle swarm optimization algorithm (MGPSO) many-objective optimization problems (MaOPs) Single and Multi-objective Bilevel Optimization evolutionary bilevel optimization (EBO) Multi-objective combinatorial optimization problems (MOCOPs) Folded Cross Regression Model (FCRM)