Evolutionary Computation 2020

• Autore: Wang Gai-Ge
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (442 p.)
• Soggetto topico: Technology: general issues
• Soggetto non controllato: 0-1 knapsack problem; ant colony optimization; assortative mating; binary whale optimization algorithm; bug detection; bWOA-S; bWOA-V; citation; classification; coevolution; constrained optimization; cuckoo search algorithm; decomposition-based multi-objective optimisation; differential evolution; dimensionality reduction; discrete artificial bee colony algorithm; diversity preservation; dominance; dynamic learning; elephant herding optimization; engineering optimization; evolutionary algorithm; evolutionary algorithms (EAs); evolutionary computation; feature selection; fuzzing; fuzzy hybrid flow shop scheduling; game feature; game simulation; game trees; geoelectric model; global optimization; green shop scheduling; grey wolf optimizer; h-index; iterated local search; knapsack problem; knowledge transfer; krill herd; magnetotelluric; many-objective optimization; memetic algorithm; menu planning problem; metaheuristic; minimize makespan; minimize total energy consumption; multi-indicators; multi-metric; multi-objective optimization; multi-resources; multi-task evolutionary computation; multi-task optimization; mutation; one-dimensional inversions; opposite path; opposition-based learning; optimization problem; Pareto optimality; Pareto-front; particle swarm optimization; path discovery; performance indicators; playtesting; playtesting metric; premature convergence; Q-learning; quantum; quantum computing; ranking; seed schedule; self-adaptive step size; simulated annealing; single objective optimization; single-objective optimization; success-history; swarm intelligence; traveling salesman problems; travelling salesman problem; turning-based mutation; unified search space; universities ranking; validation; whale optimization algorithm; WOA
• Persona (resp. second.): AlaviAmir; WangGai-Ge
• Sommario/riassunto: Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.
• Titolo autorizzato: Evolutionary Computation 2020
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557699203321
• Lo trovi qui: Univ. Federico II