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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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11#
發(fā)表于 2025-3-23 10:54:34 | 只看該作者
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發(fā)表于 2025-3-23 15:23:28 | 只看該作者
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發(fā)表于 2025-3-23 21:04:02 | 只看該作者
Maximizing the Effect of Local Disturbance in the Dynamics of Opinion Formationmarket share in advertising etc. By introducing local disturbance in the social network, such as the implantation of an agent, we can use numerical simulation to measure the effect of this agent on the result of the election, which has a deadline. By extending the statistical physics of damage sprea
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發(fā)表于 2025-3-24 01:38:08 | 只看該作者
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發(fā)表于 2025-3-24 04:06:36 | 只看該作者
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發(fā)表于 2025-3-24 07:41:33 | 只看該作者
17#
發(fā)表于 2025-3-24 11:42:12 | 只看該作者
https://doi.org/10.1007/978-981-287-588-4is 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.
18#
發(fā)表于 2025-3-24 17:19:46 | 只看該作者
Academic Literacy Across the Curriculuments 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.
19#
發(fā)表于 2025-3-24 19:27:20 | 只看該作者
Fitness Functions Evaluation for Segmentation of Lymphoma Histological Images Using Genetic Algorithetic algorithm. Qualitative and quantitative analyses allowed the definition of the Rényi entropy as the most adequate for this application. Images classification has reached results of 98.14% through accuracy metric by using this fitness function.
20#
發(fā)表于 2025-3-25 02:55:39 | 只看該作者
Improving Multi-objective Evolutionary Influence Maximization in Social Networks on three publicly available real-world networks, where we show that the evolutionary algorithm is able to improve upon the solutions found by the heuristics, while also converging faster than an evolutionary algorithm started from scratch.
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