Evolving artificial neural network ensembles
Evolving artificial neural network ensembles
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DOI：
10.1109/mci.2007.913386
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发表时间：
2008-02-01
影响因子：
9
通讯作者：
Islam, Md. Monirul
中科院分区：
文献类型：
--
作者：
Reynolds, Robert G.;Ali, Mostafa;Islam, Md. Monirul
使用一组协调的简单求解器来解决复杂问题并不是一个全新的想法。它的根源可以追溯到几百年前，当时中国古代提出了一种解决问题的团队方法。长期以来，工程师们使用分治策略将复杂的问题分解为更简单的子问题，然后由一组求解器来解决它们。然而，知道将复杂问题划分为简单问题的最佳方法在很大程度上依赖于可用的领域知识。这通常是由经验丰富的工程师进行的手动过程。文献中报道的自动分治方法很少。幸运的是，进化计算为自动分治方法提供了一些有趣的途径[15]。对这些方法的深入研究表明，进化计算和ANN集成之间存在着深层的联系。一个领域的想法可以有效地转移到另一个领域，以产生有效的算法。例如，使用物种形成来创建和保持多样性[15]启发了ANN集成的负相关学习的发展[33]，[34]以及对集成多样性的深入研究[12]，[51]。本文将回顾一些最近的工作进化方法设计人工神经网络集成。
Using a coordinated group of simple solvers to tackle a complex problem is not an entirely new idea. Its root could be traced back hundreds of years ago when ancient Chinese suggested a team approach to problem solving. For a long time, engineers have used the divide-and-conquer strategy to decompose a complex problem into simpler sub-problems and then solve them by a group of solvers. However, knowing the best way to divide a complex problem into simpler ones relies heavily on the available domain knowledge. It is often a manual process by an experienced engineer. There have been few automatic divide-and-conquer methods reported in the literature. Fortunately, evolutionary computation provides some of the interesting avenues to automatic divide-and-conquer methods [15]. An in-depth study of such methods reveals that there is a deep underlying connection between evolutionary computation and ANN ensembles.. Ideas in one area can be usefully transferred into another in producing effective algorithms. For example, using speciation to create and maintain diversity [15] had inspired the development of negative correlation learning for ANN ensembles [33], [34] and an in-depth study of diversity in ensembles [12], [51]. This paper will review some of the recent work in evolutionary approaches to designing ANN ensembles.