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Titlebook: Applications of Evolutionary Computation; 27th European Confer Stephen Smith,Jo?o Correia,Christian Cintrano Conference proceedings 2024 Th

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21#
發(fā)表于 2025-3-25 06:51:48 | 只看該作者
22#
發(fā)表于 2025-3-25 08:25:33 | 只看該作者
Absolute Space in Natural Philosophyfor fire suppression, after a breakout of fire. In our model, we have a grid graph . that represents the discretization of a terrain into cells and an ignition node . from which the fire spreads to other nodes. The spread of the fire is defined by the arc weights, which can be used to model importan
23#
發(fā)表于 2025-3-25 11:54:42 | 只看該作者
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發(fā)表于 2025-3-25 16:28:36 | 只看該作者
25#
發(fā)表于 2025-3-25 20:19:20 | 只看該作者
Distances on Surfaces and Knotsd variants on two neural network training tasks, one for image classification through the newly introduced Forward-Forward paradigm, and one for a Reinforcement Learning problem, as well as five benchmark functions for numerical optimization.
26#
發(fā)表于 2025-3-26 03:09:48 | 只看該作者
Generalizations of Metric Spacese the optimization problem. A comparative analysis shows that the proposed EA is able to find effective solutions for different scenarios, and that an island-based version of it outperforms the other two algorithms in terms of solution quality.
27#
發(fā)表于 2025-3-26 04:46:25 | 只看該作者
Distances on Strings and Permutations each region. A new central point is proposed in the subsequent phase, leveraging cluster centres for incorporation into the population. Our C2L-DE algorithm is compared against several recent DE-based neural network training algorithms, and is shown to yield favourable performance.
28#
發(fā)表于 2025-3-26 11:39:21 | 只看該作者
Low-Memory Matrix Adaptation Evolution Strategies Exploiting Gradient Information and?Lévy Flightd variants on two neural network training tasks, one for image classification through the newly introduced Forward-Forward paradigm, and one for a Reinforcement Learning problem, as well as five benchmark functions for numerical optimization.
29#
發(fā)表于 2025-3-26 15:46:54 | 只看該作者
Evolutionary Algorithms for?Optimizing Emergency Exit Placement in?Indoor Environmentse the optimization problem. A comparative analysis shows that the proposed EA is able to find effective solutions for different scenarios, and that an island-based version of it outperforms the other two algorithms in terms of solution quality.
30#
發(fā)表于 2025-3-26 18:03:20 | 只看該作者
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