Evolutionary Programming and Neural Computation (Computer and Information Science)

进化规划的目标是开发出能够通过变异和选择算法学习如何执行关键计算任务的系统。本研究采用的方法是基于这样一种认识，即一些结构性组织非常适合通过进化来学习，而另一些组织则不适合。一个通过模拟促进生物系统进化的结构-时尚关系来实现可进化性的系统已经被开发出来。目前，该系统由动态神经元的模拟网络组成，其输入输出行为由膜蛋白控制。变异和选择学习算法作用于蛋白质的分布。组织非常适合进化，因为改进总是可以通过单一的、合理可能的变异算子的应用来实现。神经元所表现出的连续动态特性也增强了系统的泛化能力。该系统被称为分子设计，因为它的灵感来自神经元的生物物理和生化特性。然而，新的努力将关注虚拟实现，这些实现本身将是有用的，并且可以为自然信息处理提供一些见解。具体目标是：纳入更丰富的非线性和细胞自动化动态，增加内存操作能力，实现系统作为高级视觉和过程控制任务的预处理器，并通过对动态应用变异和选择算子来创建能够长期进化改进的系统。

The goal of evolutionary programming is to develop systems which learn how to perform critical computing tasks through a variation and selection algorithm. The appproach taken in this research is based on the recognition that some structural organizations are well-suited to learning through evolution, while others are not. A system that achieves evolvability by simulating the structure-fashion relations that facilitate evolution in biological systems has been developed. Currently the system consists of simulated networks of dynamic neurons whose input-output behavior is controlled by membrane proteins. The variation and selection learning algorithm acts on the distribution of proteins. The organization is well-suited to evolution because improvement can always be achieved through single, reasonably likely application of the variation operator. The continuous dynamics displayed by the neurons also enhances the generalization capability of the system. The system is called a molecular design since it was inspired by biophysical and biochemical properties of neurons. However, the new efforts will be concerned with virtual implementations that would be useful in and of themselves and which could provide some insight into natural information processing. Specific objectives are: to incorporate richer nonlinear and cell automation dynamics, to add a memory manipulation capability, to implement the system as a preprocessor for high-level vision and process control tasks, and to create systems capable of long-term evolutionary improvement by applying the variation and selection operators to the dynamics.