Evolutionary Computation 2020
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 optimiza...
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (442 p.) |
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Wang, Gai-Ge edt Evolutionary Computation 2020 Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 1 electronic resource (442 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. English Technology: general issues bicssc global optimization cuckoo search algorithm Q-learning mutation self-adaptive step size evolutionary computation playtesting game feature game simulation game trees playtesting metric validation Pareto optimality h-index ranking dominance Pareto-front multi-indicators multi-metric multi-resources citation universities ranking swarm intelligence simulated annealing krill herd particle swarm optimization quantum elephant herding optimization engineering optimization metaheuristic constrained optimization multi-objective optimization single objective optimization differential evolution success-history premature convergence turning-based mutation opposition-based learning ant colony optimization opposite path traveling salesman problems whale optimization algorithm WOA binary whale optimization algorithm bWOA-S bWOA-V feature selection classification dimensionality reduction menu planning problem evolutionary algorithm decomposition-based multi-objective optimisation memetic algorithm iterated local search diversity preservation single-objective optimization knapsack problem travelling salesman problem seed schedule many-objective optimization fuzzing bug detection path discovery evolutionary algorithms (EAs) coevolution dynamic learning performance indicators magnetotelluric one-dimensional inversions geoelectric model optimization problem multi-task optimization multi-task evolutionary computation knowledge transfer assortative mating unified search space quantum computing grey wolf optimizer 0-1 knapsack problem green shop scheduling fuzzy hybrid flow shop scheduling discrete artificial bee colony algorithm minimize makespan minimize total energy consumption 3-0365-2394-4 3-0365-2395-2 Alavi, Amir edt Wang, Gai-Ge oth Alavi, Amir oth |
| language |
English |
| format |
eBook |
| author2 |
Alavi, Amir Wang, Gai-Ge Alavi, Amir |
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Alavi, Amir Wang, Gai-Ge Alavi, Amir |
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g g w ggw a a aa |
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HerausgeberIn Sonstige Sonstige |
| title |
Evolutionary Computation 2020 |
| spellingShingle |
Evolutionary Computation 2020 |
| title_full |
Evolutionary Computation 2020 |
| title_fullStr |
Evolutionary Computation 2020 |
| title_full_unstemmed |
Evolutionary Computation 2020 |
| title_auth |
Evolutionary Computation 2020 |
| title_new |
Evolutionary Computation 2020 |
| title_sort |
evolutionary computation 2020 |
| publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
| publishDate |
2021 |
| physical |
1 electronic resource (442 p.) |
| isbn |
3-0365-2394-4 3-0365-2395-2 |
| illustrated |
Not Illustrated |
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AT wanggaige evolutionarycomputation2020 AT alaviamir evolutionarycomputation2020 |
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