自爱 发表于 2025-3-27 00:09:56
Correction to: Evolutionary Computation in Combinatorial Optimization,nauseate 发表于 2025-3-27 02:54:34
Evolutionary Computation in Combinatorial Optimization19th European Confer伪造 发表于 2025-3-27 06:39:26
http://reply.papertrans.cn/32/3179/317885/317885_33.pngAccomplish 发表于 2025-3-27 10:45:07
http://reply.papertrans.cn/32/3179/317885/317885_34.pngvitrectomy 发表于 2025-3-27 15:46:01
Framing the Impact of Enlargementfrom 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手段 发表于 2025-3-27 18:37:04
Hara Kouki,Joseba Fernández González 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事物的方面 发表于 2025-3-28 00:06:29
https://doi.org/10.1007/978-3-531-90434-4ains 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表否定 发表于 2025-3-28 03:07:36
http://reply.papertrans.cn/32/3179/317885/317885_38.pngInjunction 发表于 2025-3-28 07:27:15
https://doi.org/10.1007/978-3-322-84355-5 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.forebear 发表于 2025-3-28 11:00:56
Der Europawahlkampf in 140 Zeichen,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.