This paper describes a method that automatically searches Artificial Neural Networks using Cellular Genetic Algorithms. The main difference of this method for a common genetic algorithm is the use of a cellular automaton capable of providing the location for individuals, reducing the possibility of local minima in search space. This method employs an evolutionary search for simultaneous choices of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the developed method can find compact, efficient networks with a satisfactory generalization power and with shorter training times when compared to other methods found in the literature.
翻译:本文介绍了一种使用细胞遗传算法自动搜索人造神经网络的方法。这种方法在通用遗传算法方面的主要区别是使用一个细胞自动图,能够为个人提供位置,减少在搜索空间使用当地微型模型的可能性。这种方法采用渐进式搜索方法,同时选择初始重量、转移功能、结构图和学习规则。实验结果表明,开发的方法可以找到精密高效的网络,具有令人满意的普及能力,与文献中发现的其他方法相比,培训时间较短。