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Cooperative Coevolvable Hardware and Its Applications

协同协同演化硬件及其应用

基本信息

批准号：
10650375
负责人：
SAITO Kazuyuki
金额：
$ 2.56万
依托单位：
The University of Aizu
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 1999
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10650375/
关键词：
Evolutionary Algorithm Neural Network CCEA FPGA

项目摘要

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

本研究的理论基础是赵目前提出的合作协同进化算法社会模型（CoopCEAs）。 CoopCEA是一种新的进化计算范式，许多研究人员认为它更适合解决大规模复杂问题。我们的最终目标是在硬件上实现社会模型，使实用系统能够以闪电般的速度进化。然而，这个目标在两年内实现起来太困难了。原因之一是社会模式仍有一些悬而未决的问题有待解决。第二个原因是目前的硬件（比如FPGA）技术还不足以实现这样的基因育种。基于这些考虑，我们将注意力集中在两个主题上。一是让社会模型理论上更加完善，硬件上更加可实现。另一个是实现一个简化的社会模型，并将其应用于解决简单的模式识别问题。这两个主题是并行研究的。对于第一部分，我们将源代码从C修改为C++，以便所有对象都由软件组件实现，将来可以很容易地由硬组件修改和实现。然后我们研究了合作模块的评估，这是使用社会模型要解决的核心问题。虽然我们通过实验得到了一些结果，但总体上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很多模块。一般来说，我们在使用社会模型时不必显式地分解系统，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培育系统。但在实践中，如果我们能够对系统进行适当的分解，则可以大大加速演化。为此，我们研究了决策树和最近邻多层感知器的进化学习。我们刚刚得到了一些重要的线索。预计在不久的将来会报告有趣的结果。至于第二部分，我们使用 FPGA (Altera Flex10K100) 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。该芯片经过较小的修改，可用于简单的协同进化算法。这将在下一步进行验证。较少的