Greedy Dynamic Crossover Management in Hardware Accelerated Genetic Algorithm Implementations Using FPGA

Date
2009-01-01
Authors
Kher, Shubhalaxmi
Ganesh, T. S.
Somani, Arun
Ramesh, Prem
Somani, Arun
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Abstract

Genetic algorithms are robust parallel calculation methods based on natural selection. Various crossover and mutation methods to accomplish Genetic Algorithm (GA), namely, single point, multipoint, uniform, greedy, migration, and on-demand etc.; exist. However, these mechanisms are static in nature. This paper presents a dynamic crossover (DC) mechanism. We investigate its performance by implementing in hardware (FPGA) with convergence rate and higher fitness as the performance metric. The purpose of the DC concept is two fold; to achieve faster convergence and to consume lesser memory by keeping the population size static. The results indicate that for a linear and a nonlinear objective function, DC outperforms all static crossover mechanisms.

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<p>This is a manuscript of a proceeding published as Kher, Shubhalaxmi, T. S. Ganesh, Prem Ramesh, and Arun K. Somani. "Greedy dynamic crossover management in hardware accelerated genetic algorithm implementations using FPGA." In <em>2009 11th International Conference on Computer Modelling and Simulation </em>(2009): 47-52. DOI: <a href="https://doi.org/10.1109/UKSIM.2009.119" target="_blank">10.1109/UKSIM.2009.119</a>. Posted with permission.</p>
Keywords
Genetic Algorithm, Single-Point Crossover, Fixed point crossover, Dynamic Crossover, Fitness Function, FPGA
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