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Titlebook: Knowledge Incorporation in Evolutionary Computation; Yaochu Jin Book 20051st edition Springer-Verlag Berlin Heidelberg 2005 Case-Based Rea

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樓主: 萬(wàn)靈藥
51#
發(fā)表于 2025-3-30 08:47:15 | 只看該作者
Book 20051st editionu- tionary search, into evolutionary algorithms has received increasing interest in the recent years. It has been shown from various motivations that knowl- edge incorporation into evolutionary search is able to significantly improve search efficiency. However, results on knowledge incorporation in
52#
發(fā)表于 2025-3-30 12:59:02 | 只看該作者
Methods for Using Surrogate Models to Speed Up Genetic Algorithm Optimization: Informed Operators anion by making the genetic operators more informed. The other method speeds up the optimization by genetically engineering some individuals instead of using the regular Darwinian evolution approach. Empirical results in several engineering design domains are presented.
53#
發(fā)表于 2025-3-30 17:31:36 | 只看該作者
1434-9922 evolutionary algorithms as well as knowledge representation Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu- tionary search, into evolutionary algorithms has received increasing interest in the re
54#
發(fā)表于 2025-3-30 22:17:41 | 只看該作者
55#
發(fā)表于 2025-3-31 04:04:11 | 只看該作者
A Cultural Algorithm for Solving the Job Shop Scheduling Problemoduce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.
56#
發(fā)表于 2025-3-31 08:11:04 | 只看該作者
Using Cultural Algorithms to Evolve Strategies in A Complex Agent-based System is then employed to abstract coefficients of pricing strategies that are applied to a complex model of durable goods. This model simulates consumer behaviors as applied in the context of economic cycles.
57#
發(fā)表于 2025-3-31 12:03:28 | 只看該作者
58#
發(fā)表于 2025-3-31 13:32:06 | 只看該作者
Neural Networks for Fitness Approximation in Evolutionary Optimization approximation quality of the neural networks, techniques for optimizing the structure optimization of neural networks and for generating neural network ensembles are presented. The frameworks are illustrated on benchmark problems as well as on an example of aerodynamic design optimization.
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