Genetic Optimization of Cellular Encodings for Neural Networks

[9312748] Whitley一个语法树编码了一个细胞的发展过程，这个过程可以发展出整个布尔神经网络家族。发育过程类似于生物细胞分裂。单个细胞执行由语法树指定的程序，并经历细胞分裂，以发展出能够计算特定目标函数的完整神经网络。遗传算法用于找到一个语法树，该语法树产生体系结构和权重，指定用于解决特定布尔函数（如奇偶性和对称性问题）的特定神经网络。由于细胞编码包含递归描述，因此产生的“遗传密码”比由这些代码开发的神经网络要小得多。初步研究还表明，非常简单的Hebbian学习对于加速语法树的进化是有效的。拟议的研究将继续发展语法树，为更广泛的应用指定神经网络，并开发更复杂的学习模型，与细胞发育结合使用。必须研究非布尔网络的规范，以推广可由细胞发育产生的网络类型。遗传算法被用于进化细胞发育过程的语法树；因此，将研究具有最小计算需求的学习算法

9312748 Whitley A grammar tree encodes a cellular developmental process that can develop whole families of Boolean neural networks. The development process resembles biological cell division. A single cell executes a program specified by a grammar tree and undergoes cell division to develop a complete neural network capable of computing specific target functions. A genetic algorithm is used to find a grammar tree that yields both architecture and weights specifying a particular neural network for solving specific Boolean functions, such as parity and symmetry problems. Because cellular encodings include recursive descriptions, the resulting "genetic code" is much smaller than the neural networks that are developed from these codes. Preliminary research has also shown the effectiveness of very simple Hebbian learning for speeding up the evolution of grammar trees. The proposed research will continue to develop grammar trees that specify neural networks for a wider variety of applications as well as to develop more sophisticated learning models to use in combination with cellular development. The specification of nonBoolean networks must be investigated so as generalized the types of networks that can be generated by cellular development. Genetic algorithms are being used to evolve the grammar tree for the cellular development process; as a result, learning algorithms that have minimal computational requirements will be investigated