Goblet-Cells
发表于 2025-3-30 11:25:00
https://doi.org/10.1007/978-3-540-78713-6tion. We propose a revised version of our micro-GA for multiobjective optimization which does not require any parameter fine-tuning. Furthermore, we introduce in this paper a dynamic selection scheme through which our algorithm decides which is the “best’ crossover operator to be used at any given t
euphoria
发表于 2025-3-30 14:00:36
http://reply.papertrans.cn/32/3180/317979/317979_52.png
走路左晃右晃
发表于 2025-3-30 16:55:49
http://reply.papertrans.cn/32/3180/317979/317979_53.png
membrane
发表于 2025-3-31 00:12:02
The Phenomenology of Edmund Husserl,e controllable exploration and exploitation of the decision space with a very limited number of function evaluations. The paper compares the performance of the algorithm to a typical evolutionary approach.
争议的苹果
发表于 2025-3-31 04:46:08
http://reply.papertrans.cn/32/3180/317979/317979_55.png
门窗的侧柱
发表于 2025-3-31 08:50:34
ICE: A Model of Experience with Technology,tween solutions in the non-dominated set. They also reflect the knowledge acquired by multi-objective evolutionary algorithms. A schemata-driven genetic algorithm as well as a schemata-driven local search algorithm are described. An experimental study to evaluate the suggested approach is then conducted.
伟大
发表于 2025-3-31 12:52:05
http://reply.papertrans.cn/32/3180/317979/317979_57.png
正常
发表于 2025-3-31 15:04:42
http://reply.papertrans.cn/32/3180/317979/317979_58.png
替代品
发表于 2025-3-31 17:42:21
Multiobjective Meta Level Optimization of a Load Balancing Evolutionary Algorithmfor optimizing the effectiveness and effciency of a load-balancing evolutionary algorithm. We show that the generated parameters perform statistically better than a standard set of parameters and analyze the importance of selecting a good region on the Pareto Front for this type of optimization.
玩忽职守
发表于 2025-3-31 22:27:46
Schemata-Driven Multi-objective Optimizationtween solutions in the non-dominated set. They also reflect the knowledge acquired by multi-objective evolutionary algorithms. A schemata-driven genetic algorithm as well as a schemata-driven local search algorithm are described. An experimental study to evaluate the suggested approach is then conducted.