A model-adaptive evolutionary algorithm for optimization
A model-adaptive evolutionary algorithm for optimization
复制标题
一种模型自适应进化优化算法
DOI：
10.1007/s10015-011-0987-8
复制
发表时间：
2011
影响因子：
0.9
通讯作者：
S.
中科院分区：
文献类型：
--
作者：
Tenne;Y.;Izui;K.;Nishiwaki;S.
工程和科学中的许多应用依赖于计算昂贵的函数的优化。在这种情况下，一个成功的方法是耦合的进化算法与数学模型，取代昂贵的功能。然而，模型引入了一些困难，例如它们固有的不准确性，以及将模型与特定问题相匹配的困难。为了解决这些问题，本文提出了一种基于模型的进化算法，主要有两个实现：（a）其利用定制的信赖域方法来对抗模型不准确性，以在搜索期间管理模型，并确保收敛到真正昂贵函数的最优值，以及（B）在搜索期间，其从一组候选模型中连续地选择最优模型类型，导致模型自适应优化搜索。广泛的性能分析表明所提出的算法的有效性。
Many applications in engineering and science rely on the optimization of computationally expensive functions. A successful approach in such scenarios is to couple an evolutionary algorithm with a mathematical model which replaces the expensive function. However, models introduce several difficulties, such as their inherent inaccuracy, and the difficulty of matching a model to a particular problem. To address these issues, this paper proposes a model-based evolutionary algorithm with two main implementations: (a) it combats model inaccuracy with a tailored trust-region approach to manage the model during the search, and to ensure convergence to an optimum of the true expensive function, and (b) during the search it continuously selects an optimal model type out of a set of candidate models, resulting in a model-adaptive optimization search. Extensive performance analysis shows the efficacy of the proposed algorithm.
具有异构演化的自动模型类型选择：射频电路模块建模的应用
DOI：
10.1109/cec.2008.4630917
发表时间：
2008
期刊：
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
影响因子：
--
作者：
D. Gorissen;L. D. Tommasi;J. Croon;T. Dhaene
通讯作者：
T. Dhaene