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Titlebook: Evolutionary Multi-Criterion Optimization; Second International Carlos M. Fonseca,Peter J. Fleming,Kalyanmoy Deb Conference proceedings 200

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11#
發(fā)表于 2025-3-23 12:27:39 | 只看該作者
The Two Logics and their Relation,gorithms. We propose two particular strategies and discuss combinations of those which lead to a better algorithmic performance. Finally we illustrate the efficiency of our methods by several examples.
12#
發(fā)表于 2025-3-23 15:49:28 | 只看該作者
13#
發(fā)表于 2025-3-23 18:10:11 | 只看該作者
https://doi.org/10.1007/978-3-540-78713-6ime. Such a scheme has helped to improve the performance of the new version of the algorithm which is called the micro-GA2 (.GA.). The new approach is validated using several test function and metrics taken from the specialized literature and it is compared to the NSGA-II and PAES.
14#
發(fā)表于 2025-3-24 00:15:54 | 只看該作者
15#
發(fā)表于 2025-3-24 02:59:07 | 只看該作者
Solving Hierarchical Optimization Problems Using MOEAsbjective-spaces, we apply genetic operators also on the structure of hierarchical chromosomes. This novel approach decreases exploration time substantially. The example of system synthesis is used as a case study to illustrate the necessity and the benefits of ..
16#
發(fā)表于 2025-3-24 08:27:28 | 只看該作者
The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimizationime. Such a scheme has helped to improve the performance of the new version of the algorithm which is called the micro-GA2 (.GA.). The new approach is validated using several test function and metrics taken from the specialized literature and it is compared to the NSGA-II and PAES.
17#
發(fā)表于 2025-3-24 12:05:21 | 只看該作者
The Maximin Fitness Function; Multi-objective City and Regional Planningfunctions are briefly compared to a state-of-the-art fitness function from the literature. Results from a real-world multi-objective problem are presented. This problem addresses land-use and transportation planning for high-growth cities and metropolitan regions.
18#
發(fā)表于 2025-3-24 15:14:16 | 只看該作者
Use of a Genetic Heritage for Solving the Assignment Problem with Two Objectivesapproximate non-supported solutions. Bound sets describe one acceptable limit for applying a local search over an offspring. Results of extensive numerical experiments are reported. All exact efficient solutions are obtained using Cplex in a basic enumerative procedure. A comparison with published results shows the efficiency of this approach.
19#
發(fā)表于 2025-3-24 21:12:14 | 只看該作者
IS-PAES: A Constraint-Handling Technique Based on Multiobjective Optimization Conceptsve grid as the original PAES (Pareto Archived Evolution Strategy). However, the adaptive grid of IS-PAES does not have the serious scalability problems of the original PAES. The proposed constraint-handling approach is validated with several examples taken from the standard literature on evolutionary optimization.
20#
發(fā)表于 2025-3-25 01:37:46 | 只看該作者
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