Cooperative Coevolvable Hardware and Its Applications

本研究的理论基础是目前由Zhao等人提出的合作协同进化算法的社会模型（CoopCEAs）。CoopCEA是一种新的进化计算范式，许多研究者认为它更适合于解决大规模和复杂的问题。我们的最终目标是在硬件中实现社会模型，以便实际系统可以以闪电般的速度发展。但是，这个目标在两年内难以实现。原因之一是，关于社会模式，仍然有一些悬而未决的问题需要解决。第二个原因是，目前的硬件（如FPGA）技术还不足以实现这样一个基因育种器。基于这些考虑，我们把注意力集中在两个主题。一是使社会模式在理论上更加健全，在硬件上更加可操作。另一个是实现一个简化的社会模型，并应用它来解决一个简单的模式识别问题。 ...更多信息 em.这两个课题是并行研究的，第一部分将源代码从C语言修改为C++语言，使所有对象都由软件构件实现，这样以后就可以方便地修改和用硬构件实现。然后研究了社会模型应用中需要解决的核心问题--合作模块的评价问题。虽然我们通过实验得到了一些结果，但我们仍然不清楚如何更有效地使用CoopCEA。另一个关键点是如何将一个大系统分解成许多模块。一般来说，我们不必在使用社会模型时显式地分解系统，因为一般来说，CoopCEA可以使用尚未进化的组件和尚未进化的结构来繁殖系统。然而，在实践中，如果我们能够适当地分解系统，进化可以大大加速。为此，我们研究了决策树和最近邻多层感知器的进化学习。我们刚刚得到一些重要的线索。在第二部分中，我们用FPGA（Altera Flex 10 K100）实现了一个简单的基于最近邻的神经网络。它通过PCT总线连接到主机。所有的遗传操作都在计算机中进行，评估部分在FPGA芯片上进行。该芯片，与小的修改，可以用于简单的协同进化算法。这将在下一步进行验证。少

The theoretic foundation of this research is a Society model for cooperative co-evolutionary algorithms (CoopCEAs), which was proposed currently by Zhao. CoopCEA is a new paradigm for evolutionary computation, which is, as believed by many researchers, more suitable for solving large-scale and complex problems. Our final goal is to realize the society model in hardware, so that practical systems can be evolved in lightning speed. This goal, however, is too difficult to be achieved in two years. One reason is that there are still a few open problems to be solved concerning the societv model. The second reason is that current hardware (say, FPGA) technology is still not sufficient for realizing such a genetic breeder.Based on these considerations, we have focused our attention on two topics. One is to make the society model more theoretically sound and more hardware implementable. Another is to implement a simplified society model, and apply it to solve a simple pattern recognition probl … More em. These two topics were studied in parallel.As for the first part, we modified our source-code from C to C++, so that all objects are realized by software components, which can be easily modified and implemented by hard components in the future. We then studied the evaluation of the cooperative modules, which is the core problem to be solved in using the society model. Although we got some results through experiments, we are still not clear how to use CoopCEA more effectively in general. Another key point is how to decompose a large system into many modules. In general, we do not have to decompose the system explicitly in using the society model, because in general, the CoopCEAs can breed systems using yet-to-be-evolved components and yet-to-be-evolved structures. In practice, however, if we can decompose the system properly, the evolutioncan be greatly accelerated. For this purpose, we studied evolutionary learning of decision trees and nearestneighbor multilayer perceptrons. We have just got some important clues. Interesting results are expected to be reported in near future.As for second part, we implemented a simple nearest neithbor based neural network usint FPGA (Altera Flex10K100). It is connected through PCT bus to a host computer. All genetic operations are performed in the computer, and the evaluation part is performed on the FPGA chip. The chip, with minor revision,could be used for the simple Co-Evolutionary algorithm. This will be verified in the next step. Less