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Titlebook: Evolutionary Computation in Combinatorial Optimization; 19th European Confer Arnaud Liefooghe,Luís Paquete Conference proceedings 2019 Spri

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樓主: decoction
41#
發(fā)表于 2025-3-28 16:18:34 | 只看該作者
42#
發(fā)表于 2025-3-28 22:02:33 | 只看該作者
43#
發(fā)表于 2025-3-28 23:05:36 | 只看該作者
A Cooperative Optimization Approach for Distributing Service Points in Mobility Applications,from the feedback of potential users given to candidate solutions. For the actual optimization we consider a population based iterated greedy algorithm. Experiments on artificial benchmark scenarios with idealized simulated user behavior show the learning capabilities of the surrogate objective func
44#
發(fā)表于 2025-3-29 06:24:32 | 只看該作者
A New Representation in Genetic Programming for Evolving Dispatching Rules for Dynamic Flexible Job in the terminal set have different contributions to the decision making. However, the current GP approaches cannot perfectly find proper combinations between the features in accordance with their contributions. In this paper, we propose a new representation for GP that better considers the differen
45#
發(fā)表于 2025-3-29 10:53:21 | 只看該作者
An Iterated Local Search Algorithm for the Two-Machine Flow Shop Problem with Buffers and Constant ains better results than two state-of-the-art algorithms for buffered flow shop problems from the literature and an Ant Colony Optimization algorithm. In addition, it is shown experimentally that 2BF-ILS can obtain the same solution quality as the standard NEH heuristic with a smaller number of func
46#
發(fā)表于 2025-3-29 14:16:56 | 只看該作者
47#
發(fā)表于 2025-3-29 16:18:10 | 只看該作者
Multiple Periods Vehicle Routing Problems: A Case Study, than 6000 customers and 69951 requests of visits. The results show an excellent performance of the solving approach in terms of solution quality compared with the existing plan used by the hygiene services company.
48#
發(fā)表于 2025-3-29 22:49:35 | 只看該作者
Rigorous Performance Analysis of State-of-the-Art TSP Heuristic Solvers,cision trees are used to identify main features which could best inform algorithm selection. The most prominent features identified a high proportion of instances where the GA with Edge Assembly Crossover performed significantly better when solving to optimality.
49#
發(fā)表于 2025-3-30 02:59:43 | 只看該作者
50#
發(fā)表于 2025-3-30 05:42:08 | 只看該作者
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