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Titlebook: Applications of Evolutionary Computation; 21st International C Kevin Sim,Paul Kaufmann Conference proceedings 2018 Springer International P

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21#
發(fā)表于 2025-3-25 04:02:41 | 只看該作者
A Fast Metaheuristic for the Design of?DVB-T2 Networksis developed to tackle the unsatisfying performance of state-of-the-art optimization solvers when adopted to solve realistic instances. Computational tests on realistic instances show that our metaheuristic can find solutions of much better quality than those identified by a state-of-the-art optimization solver.
22#
發(fā)表于 2025-3-25 11:02:00 | 只看該作者
Accelerating the Computation of?Solutions in Resource Allocation Problems Using an Evolutionary Apprents and the system authority exchange solutions that are incorporated by the other party. The contributions are twofold: we propose a general scheme for such synergy and show its benefits in scenarios related to congestion games.
23#
發(fā)表于 2025-3-25 13:04:30 | 只看該作者
Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Comlter-wrapper hybrid based on Iterated Local Search and Mutual Information, and shows that it can provide more reliable solutions, where the solutions are more able to generalise to unseen data. This study further contributes comparisons over multiple datasets, something that has been uncommon in the literature.
24#
發(fā)表于 2025-3-25 18:16:02 | 只看該作者
25#
發(fā)表于 2025-3-25 21:01:46 | 只看該作者
Multimodal Transportation Network Design Using Physarum Polycephalum-Inspired Multi-agent Computatioerformance of resultant multimodal networks against single mode networks involving the same component modes is demonstrated. The observations point to the potential applicability of the new approach in city planning and design.
26#
發(fā)表于 2025-3-26 02:07:15 | 只看該作者
27#
發(fā)表于 2025-3-26 05:27:05 | 只看該作者
28#
發(fā)表于 2025-3-26 11:52:39 | 只看該作者
A Genetic Algorithm for Community Detection in Attributed Graphsty. Experiments on synthetic networks and a comparison with five state-of-the-art methods show that the genetic approach is very competitive and obtains network divisions more accurate than those obtained by the considered methods.
29#
發(fā)表于 2025-3-26 14:20:59 | 只看該作者
30#
發(fā)表于 2025-3-26 19:47:30 | 只看該作者
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