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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| 100 | 1 | |a Wang, Gai-Ge |4 edt | |
| 245 | 1 | 0 | |a Evolutionary Computation 2020 |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (442 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 520 | |a 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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 653 | |a global optimization | ||
| 653 | |a cuckoo search algorithm | ||
| 653 | |a Q-learning | ||
| 653 | |a mutation | ||
| 653 | |a self-adaptive step size | ||
| 653 | |a evolutionary computation | ||
| 653 | |a playtesting | ||
| 653 | |a game feature | ||
| 653 | |a game simulation | ||
| 653 | |a game trees | ||
| 653 | |a playtesting metric | ||
| 653 | |a validation | ||
| 653 | |a Pareto optimality | ||
| 653 | |a h-index | ||
| 653 | |a ranking | ||
| 653 | |a dominance | ||
| 653 | |a Pareto-front | ||
| 653 | |a multi-indicators | ||
| 653 | |a multi-metric | ||
| 653 | |a multi-resources | ||
| 653 | |a citation | ||
| 653 | |a universities ranking | ||
| 653 | |a swarm intelligence | ||
| 653 | |a simulated annealing | ||
| 653 | |a krill herd | ||
| 653 | |a particle swarm optimization | ||
| 653 | |a quantum | ||
| 653 | |a elephant herding optimization | ||
| 653 | |a engineering optimization | ||
| 653 | |a metaheuristic | ||
| 653 | |a constrained optimization | ||
| 653 | |a multi-objective optimization | ||
| 653 | |a single objective optimization | ||
| 653 | |a differential evolution | ||
| 653 | |a success-history | ||
| 653 | |a premature convergence | ||
| 653 | |a turning-based mutation | ||
| 653 | |a opposition-based learning | ||
| 653 | |a ant colony optimization | ||
| 653 | |a opposite path | ||
| 653 | |a traveling salesman problems | ||
| 653 | |a whale optimization algorithm | ||
| 653 | |a WOA | ||
| 653 | |a binary whale optimization algorithm | ||
| 653 | |a bWOA-S | ||
| 653 | |a bWOA-V | ||
| 653 | |a feature selection | ||
| 653 | |a classification | ||
| 653 | |a dimensionality reduction | ||
| 653 | |a menu planning problem | ||
| 653 | |a evolutionary algorithm | ||
| 653 | |a decomposition-based multi-objective optimisation | ||
| 653 | |a memetic algorithm | ||
| 653 | |a iterated local search | ||
| 653 | |a diversity preservation | ||
| 653 | |a single-objective optimization | ||
| 653 | |a knapsack problem | ||
| 653 | |a travelling salesman problem | ||
| 653 | |a seed schedule | ||
| 653 | |a many-objective optimization | ||
| 653 | |a fuzzing | ||
| 653 | |a bug detection | ||
| 653 | |a path discovery | ||
| 653 | |a evolutionary algorithms (EAs) | ||
| 653 | |a coevolution | ||
| 653 | |a dynamic learning | ||
| 653 | |a performance indicators | ||
| 653 | |a magnetotelluric | ||
| 653 | |a one-dimensional inversions | ||
| 653 | |a geoelectric model | ||
| 653 | |a optimization problem | ||
| 653 | |a multi-task optimization | ||
| 653 | |a multi-task evolutionary computation | ||
| 653 | |a knowledge transfer | ||
| 653 | |a assortative mating | ||
| 653 | |a unified search space | ||
| 653 | |a quantum computing | ||
| 653 | |a grey wolf optimizer | ||
| 653 | |a 0-1 knapsack problem | ||
| 653 | |a green shop scheduling | ||
| 653 | |a fuzzy hybrid flow shop scheduling | ||
| 653 | |a discrete artificial bee colony algorithm | ||
| 653 | |a minimize makespan | ||
| 653 | |a minimize total energy consumption | ||
| 776 | |z 3-0365-2394-4 | ||
| 776 | |z 3-0365-2395-2 | ||
| 700 | 1 | |a Alavi, Amir |4 edt | |
| 700 | 1 | |a Wang, Gai-Ge |4 oth | |
| 700 | 1 | |a Alavi, Amir |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:42:18 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338132270004498&Force_direct=true |Z 5338132270004498 |b Available |8 5338132270004498 | ||