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https://doi.org/10.1007/978-3-642-30671-6Computational Intelligence; Hybrid Metaheuristics
倾听
发表于 2025-3-27 13:05:03
Hybridizing Cellular GAs with Active Components of Bio-inspired Algorithmsim to achieve significant numerical improvements in the results obtained by a cGA in combinatorial optimization problems. We here analyze the performance of our hybrids using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency.
不再流行
发表于 2025-3-27 14:12:21
Book 2013 design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuris
Crayon
发表于 2025-3-27 18:26:51
Hybrid Metaheuristics for Medical Data Classification less difficult. Heuristics and meta-heuristics are normally applied to approximate its solution. This chapter reviews hybrid meta-heuristics for medical data classification task, particularly diagnosis and prognosis, and their application to model selection, including parameter optimization and feature subset selection.
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发表于 2025-3-28 00:04:11
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虚情假意
发表于 2025-3-28 06:04:11
A Multi-paradigm Tool for Large Neighborhood Searchid search. In order to test the tool, we show the development phase of hybrid solvers on some benchmark problems. Moreover, we compare these solvers with other approaches, namely a pure Local Search, a pure constraint programming search, and with a state-of-the-art solver for constraint-based Local Search.
平躺
发表于 2025-3-28 10:07:08
Book 2013tics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling..
N斯巴达人
发表于 2025-3-28 11:37:37
Hybridizations of GRASP with Path-Relinkingmum found is returned as the solution of path-relinking. The hybridization of path-relinking and GRASP adds memory mechanisms to GRASP. This chapter describes basic concepts of GRASP, path-relinking, and the hybridization of GRASP with path-relinking.