Evolutionary Computation
Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biolog...
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| Year of Publication: | 2019 |
| Language: | English |
| Physical Description: | 1 electronic resource (424 p.) |
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| 100 | 1 | |a Alavi, Amir |4 auth | |
| 245 | 1 | 0 | |a Evolutionary Computation |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (424 p.) | ||
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| 520 | |a Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism, | ||
| 546 | |a English | ||
| 653 | |a individual updating strategy | ||
| 653 | |a integrated design | ||
| 653 | |a global optimum | ||
| 653 | |a flexible job shop scheduling problem | ||
| 653 | |a whale optimization algorithm | ||
| 653 | |a EHO | ||
| 653 | |a bat algorithm with multiple strategy coupling (mixBA) | ||
| 653 | |a multi-objective DV-Hop localization algorithm | ||
| 653 | |a optimization | ||
| 653 | |a rock types | ||
| 653 | |a variable neighborhood search | ||
| 653 | |a biology | ||
| 653 | |a average iteration times | ||
| 653 | |a CEC2013 benchmarks | ||
| 653 | |a slicing tree structure | ||
| 653 | |a firefly algorithm (FA) | ||
| 653 | |a benchmark | ||
| 653 | |a single loop | ||
| 653 | |a evolutionary computation | ||
| 653 | |a memetic algorithm | ||
| 653 | |a normal cloud model | ||
| 653 | |a 0-1 knapsack problems | ||
| 653 | |a elite strategy | ||
| 653 | |a diversity maintenance | ||
| 653 | |a material handling path | ||
| 653 | |a artificial bee colony algorithm (ABC) | ||
| 653 | |a urban design | ||
| 653 | |a entropy | ||
| 653 | |a evolutionary algorithms (EAs) | ||
| 653 | |a monarch butterfly optimization | ||
| 653 | |a numerical simulation | ||
| 653 | |a architecture | ||
| 653 | |a set-union knapsack problem | ||
| 653 | |a Wilcoxon test | ||
| 653 | |a convolutional neural network | ||
| 653 | |a global position updating operator | ||
| 653 | |a particle swarm optimization | ||
| 653 | |a computation | ||
| 653 | |a minimum load coloring | ||
| 653 | |a topology structure | ||
| 653 | |a adaptive multi-swarm | ||
| 653 | |a minimum total dominating set | ||
| 653 | |a mutation operation | ||
| 653 | |a shape grammar | ||
| 653 | |a greedy optimization algorithm | ||
| 653 | |a ?-Hilbert space | ||
| 653 | |a genetic algorithm | ||
| 653 | |a large scale optimization | ||
| 653 | |a large-scale optimization | ||
| 653 | |a NSGA-II-DV-Hop | ||
| 653 | |a constrained optimization problems (COPs) | ||
| 653 | |a first-arrival picking | ||
| 653 | |a transfer function | ||
| 653 | |a SPEA 2 | ||
| 653 | |a stochastic ranking (SR) | ||
| 653 | |a wireless sensor networks (WSNs) | ||
| 653 | |a acceleration search | ||
| 653 | |a convergence point | ||
| 653 | |a fuzzy c-means | ||
| 653 | |a evolutionary algorithm | ||
| 653 | |a success rates | ||
| 653 | |a Artificial bee colony | ||
| 653 | |a particle swarm optimizer | ||
| 653 | |a random weight | ||
| 653 | |a range detection | ||
| 653 | |a adaptive weight | ||
| 653 | |a large-scale | ||
| 653 | |a automatic identification | ||
| 653 | |a cloud model | ||
| 653 | |a swarm intelligence | ||
| 653 | |a evolutionary multi-objective optimization | ||
| 653 | |a DV-Hop algorithm | ||
| 653 | |a bat algorithm (BA) | ||
| 653 | |a Friedman test | ||
| 653 | |a quantum uncertainty property | ||
| 653 | |a facility layout design | ||
| 653 | |a local search | ||
| 653 | |a deep learning | ||
| 653 | |a Y conditional cloud generator | ||
| 653 | |a benchmark functions | ||
| 653 | |a discrete algorithm | ||
| 653 | |a dispatching rule | ||
| 653 | |a DE algorithm | ||
| 653 | |a nonlinear convergence factor | ||
| 653 | |a energy-efficient job shop scheduling | ||
| 653 | |a t-test | ||
| 653 | |a evolution | ||
| 653 | |a dimension learning | ||
| 653 | |a global optimization | ||
| 653 | |a confidence term | ||
| 653 | |a elephant herding optimization | ||
| 653 | |a moth search algorithm | ||
| 653 | |a evolutionary | ||
| 776 | |z 3-03921-928-6 | ||
| 700 | 1 | |a Wang, Gai-Ge |4 auth | |
| 906 | |a BOOK | ||
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