Evolutionary Neuroestimation of Fitness Functions
Evolutionary Neuroestimation of Fitness Functions
复制标题
适应度函数的进化神经估计
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
D. S. Rodríguez
中科院分区：
文献类型：
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作者：
J. Aguilar;D. Mateos;D. S. Rodríguez
在进化算法的解决方案的质量的最有影响力的因素之一是适当的适应度函数。特别是在数据挖掘中，提取有用的信息是一个主要的任务，当数据库有大量的例子，适应度函数是非常耗时的。在这个意义上，对适应度值的近似可以有益于减少其相关联的计算成本。在本文中，我们提出了神经进化模型（NEM），它使用神经网络作为适应度函数估计。神经网络通过进化过程进行训练，并逐步用于估计适应度值，这提高了搜索效率，同时减轻了适应度函数的计算负担。我们证明了NEM比传统的进化算法更快，在一些假设下，神经网络进行的估计总量。当数据集包含大量示例时，神经进化模型证明是有用的。
One of the most influential factors in the quality of the solutions found by an evolutionary algorithm is the appropriateness of the fitness function. Specifically in data mining, in where the extraction of useful information is a main task, when databases have a great amount of examples, fitness functions are very time consuming. In this sense, an approximation to fitness values can be beneficial for reducing its associated computational cost. In this paper, we present the Neural–Evolutionary Model (NEM), which uses a neural network as a fitness function estimator. The neural network is trained through the evolutionary process and used progressively to estimate the fitness values, what enhances the search efficiency while alleviating the computational overload of the fitness function. We demonstrate that the NEM is faster than the traditional evolutionary algorithm, under some assumptions over the total amount of estimations carried out by the neural network. The Neural–Evolutionary Model proves then useful when datasets contain vast amount of examples.