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Proceedings Paper

A novel pipeline based FPGA implementation of a genetic algorithm
Author(s): Nonel Thirer
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Paper Abstract

To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of “natural selection”, where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.

Paper Details

Date Published: 22 May 2014
PDF: 8 pages
Proc. SPIE 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, 91190B (22 May 2014); doi: 10.1117/12.2053140
Show Author Affiliations
Nonel Thirer, Holon Institute of Technology (Israel)

Published in SPIE Proceedings Vol. 9119:
Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII
Misty Blowers; Jonathan Williams, Editor(s)

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