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On evolving hardware: on-line evolution by cellular programming

机译:在不断发展的硬件上:蜂窝编程的在线演变

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Extensive research shows that the implementation of evolutionary algorithms are costly in many cases. This paper reports on an alternative construction, namely non-uniform cellular automatons (CA) with which (1) it is possible to increase the capacity for complex computations while preserving the main features of CAs, (2) evolve parallel cellular machines to perform computational tasks via cellular programming, and (3) attain evolware with implementations centering on hardware. The hardware architecture of the cellular programming evolware involves: (1) a group of individual cells is in operation rather than a single one, (2) genetic operators are implemented on-board rather than on offline computer, and (3) the evolutionary phase proceeds uninterrupted via a continuous machine's operation. The active components of the evolware complex comprise exclusively the latest cellular FPGA circuit. Accordingly, this paper is organized as follows: Section I presents fundamentals and analysis of computations in quasiuniform cellular automats in a quasi-uniform cellular space; Section II introduces the basic approach for coevolving cellular computation and cellular machines, denoted cellular programming. Section III adopts synchronization as a criteria to attain evolware centered on hardware. Cellular programming is implemented in hardware. The algorithm is slightly modified (without loss of performance): the genetic operators, one-point crossover and mutation, are replaced with the operator uniform crossover. This results in the creation of an offspring genome from two parent genomes (bit strings) with a 50% probability for each parent. Finally, Section IV analyzes performance and results.
机译:广泛的研究表明,在许多情况下,进化算法的实施昂贵。本文报告了替代结构,即非均匀的蜂窝自动化(CA),其中(1)可以增加复杂计算的容量,同时保留CAS的主要特征,(2)演变并行蜂窝机执行计算通过蜂窝编程任务,(3)在硬件上居中实现EvolWare。蜂窝编程EvolWare的硬件架构涉及:(1)一组单个单元格在运行而不是单个,(2)遗传运算符在板上实现而不是在离线计算机上实现,并且(3)进化阶段通过连续机器的操作不间断地进行。 EvolWare复合物的有源组件仅包括最新的蜂窝FPGA电路。因此,本文组织如下:第I节介绍了准均匀的蜂窝空间中拟种细胞自动血管计算的基础和分析;第二部分介绍了跨越蜂窝计算和蜂窝机的基本方法,表示蜂窝编程。第III节采用同步作为以居中为中心的evolware的标准。蜂窝编程在硬件中实现。算法略微修改(不损失性能):遗传操作员,单点交叉和突变,用操作者均匀交叉代替。这导致从两个父基因组(位字符串)中创建后代基因组,每个父母的概率为50%。最后,第四节分析了性能和结果。

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