Computational experiments involving population size for FPGA-based implementation of a GA for the TSP

Kok, Jonathan, Kelson, Neil A., Gonzalez, Luis F., & Bruggemann, Troy S. (2012) Computational experiments involving population size for FPGA-based implementation of a GA for the TSP. In Proceedings of the 4th International Conference on Computational Methods (ICCM2012), Crowne Plaza, Gold Coast, QLD.

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Abstract

The feasibility of using an in-hardware implementation of a genetic algorithm (GA) to solve the computationally expensive travelling salesman problem (TSP) is explored, especially in regard to hardware resource requirements for problem and population sizes. We investigate via numerical experiments whether a small population size might prove sufficient to obtain reasonable quality solutions for the TSP, thereby permitting relatively resource efficient hardware implementation on field programmable gate arrays (FPGAs). Software experiments on two TSP benchmarks involving 48 and 532 cities were used to explore the extent to which population size can be reduced without compromising solution quality, and results show that a GA allowed to run for a large number of generations with a smaller population size can yield solutions of comparable quality to those obtained using a larger population. This finding is then used to investigate feasible problem sizes on a targeted Virtex-7 vx485T-2 FPGA platform via exploration of hardware resource requirements for memory and data flow operations.

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ID Code: 57033
Item Type: Conference Paper
Refereed: Yes
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > Research Centres > High Performance Computing and Research Support
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 The International Conference on Computational Methods
Deposited On: 10 Feb 2013 23:20
Last Modified: 25 Feb 2013 23:00

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