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Titlebook: Artificial Evolution; 7th International Co El-Ghazali Talbi,Pierre Liardet,Marc Schoenauer Conference proceedings 2006 Springer-Verlag Berl

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樓主: 令人不愉快
41#
發(fā)表于 2025-3-28 15:32:28 | 只看該作者
Notch Effects in Fracture and Fatigue,ntroduced recently and have been tested only for theoretical functions. We describe the application of those ES niching methods to a challenging real-life high-dimensional optimization problem, namely .. The methods are shown to be robust and to achieve satisfying results for the given problem.
42#
發(fā)表于 2025-3-28 22:01:50 | 只看該作者
43#
發(fā)表于 2025-3-29 02:18:54 | 只看該作者
Stress Distribution at Notch Tip,for the vehicle routing problem. The contribution of the introduced mechanisms is evaluated by different performance metrics. All the experimentations indicate a strict improvement of the generated Pareto set.
44#
發(fā)表于 2025-3-29 06:28:14 | 只看該作者
45#
發(fā)表于 2025-3-29 10:35:57 | 只看該作者
Stress Distribution at Notch Tip, progress is not necessarily identical to global overall progress, we extend this analysis by cross-validating individuals from different evolutionary runs, which we argue is more appropriate than single-record analysis method for evaluating the global performance of individuals.
46#
發(fā)表于 2025-3-29 15:16:20 | 只看該作者
47#
發(fā)表于 2025-3-29 18:21:08 | 只看該作者
Niching in Evolution Strategies and Its Application to Laser Pulse Shapingntroduced recently and have been tested only for theoretical functions. We describe the application of those ES niching methods to a challenging real-life high-dimensional optimization problem, namely .. The methods are shown to be robust and to achieve satisfying results for the given problem.
48#
發(fā)表于 2025-3-29 23:45:03 | 只看該作者
49#
發(fā)表于 2025-3-30 01:22:20 | 只看該作者
Enhancements of NSGA II and Its Application to the Vehicle Routing Problem with Route Balancingfor the vehicle routing problem. The contribution of the introduced mechanisms is evaluated by different performance metrics. All the experimentations indicate a strict improvement of the generated Pareto set.
50#
發(fā)表于 2025-3-30 07:25:50 | 只看該作者
A Backbone-Based Co-evolutionary Heuristic for Partial MAX-SATtion in setting a co-evolutionary stochastic local search algorithm guided by an estimated backbone variables of the problem. The effectiveness of our algorithm is examined by computational experiments. Reported results for a number of PMSAT instances suggest that this approach can outperform state-of-the-art PMSAT techniques.
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