Reprint
Evolutionary Computation
Edited by
November 2019
424 pages
- ISBN978-3-03921-928-5 (Paperback)
- ISBN978-3-03921-929-2 (PDF)
This book is a reprint of the Special Issue Evolutionary Computation that was published in
Computer Science & Mathematics
Engineering
Physical Sciences
Public Health & Healthcare
Summary
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, imitate the behavior of the animal's group, mimic the characteristics of human thought, language, and memory processes, or mimic the physical phenomena of nature, hoping to simulate the wisdom of nature and humanity enables an optimal solution to the problem and solves an acceptable solution in an acceptable time. Computational intelligent algorithms have received extensive attention at home and abroad, and have become an important research direction of artificial intelligence and computer science. This book will introduce the application of intelligent optimization algorithms in detail from the aspects of computational intelligence, job shop scheduling problems, multi-objective optimization problems, and machine learning
Format
- Paperback
License
© 2020 by the authors; CC BY license
Keywords
artificial bee colony algorithm (ABC); cloud model; normal cloud model; Y conditional cloud generator; global optimum; evolution; computation; urban design; biology; shape grammar; architecture; SPEA 2; energy-efficient job shop scheduling; dispatching rule; nonlinear convergence factor; mutation operation; whale optimization algorithm; particle swarm optimization; confidence term; random weight; benchmark functions; t-test; success rates; average iteration times; set-union knapsack problem; moth search algorithm; transfer function; discrete algorithm; evolutionary multi-objective optimization; convergence point; acceleration search; evolutionary computation; optimization; bat algorithm (BA); bat algorithm with multiple strategy coupling (mixBA); CEC2013 benchmarks; Wilcoxon test; Friedman test; facility layout design; single loop; monarch butterfly optimization; slicing tree structure; material handling path; integrated design; wireless sensor networks (WSNs); DV-Hop algorithm; multi-objective DV-Hop localization algorithm; NSGA-II-DV-Hop; first-arrival picking; fuzzy c-means; particle swarm optimization; range detection; minimum total dominating set; evolutionary algorithm; genetic algorithm; local search; constrained optimization problems (COPs); evolutionary algorithms (EAs); firefly algorithm (FA); stochastic ranking (SR); Artificial bee colony; swarm intelligence; elite strategy; dimension learning; global optimization; DE algorithm; β-Hilbert space; topology structure; quantum uncertainty property; numerical simulation; whale optimization algorithm; flexible job shop scheduling problem; nonlinear convergence factor; adaptive weight; variable neighborhood search; elephant herding optimization; EHO; swarm intelligence; individual updating strategy; large-scale; benchmark; diversity maintenance; particle swarm optimizer; entropy; large scale optimization; minimum load coloring; memetic algorithm; evolutionary; local search; particle swarm optimization; large-scale optimization; adaptive multi-swarm; diversity maintenance; deep learning; convolutional neural network; rock types; automatic identification; monarch butterfly optimization; greedy optimization algorithm; global position updating operator; 0-1 knapsack problems