A Spin Ring Crossover Operator Using FPGA


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Abstract


The genetic algorithms are adaptive search algorithms based on natural evolution and selection. The concept in genetics is based on simulating a population in a natural process that is considered to be fittest. Every individual from a population is said to undergo a specific selection rules to maximize the fitness function. To overcome the fitness function, the genetic algorithm should have an efficient crossover operator. The cross over is one of the major operation as it aims to produce an offspring with the best properties of the parents. The resultant fittest offspring termed as a new population, replaces the parent set in generational genetics.  In this paper, a new crossover operator called as spin ring crossover operator (SRC) has been proposed and implemented in hardware. The SRC is designed, simulated and implemented in vertex 2 FPGA.  The comparisons with other conventional crossover operators are carried out in terms of resource and delay parameters.
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Keywords


Population; Crossover; Offspring; Recombination; Fitness

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References


Y. Kaya, M. Uyar, R. Tekđn, A Novel Crossover Operator for Genetic Algorithms: Ring Crossover, CoRR abs/1105.0355: (2011).

S.N. Sivanandan, S. N. Deepa, Introduction to Genetic Algorithm, Springer-Verlag Berlin Heidelberg, 2008.

P. Stepaj, G. Marin, Comparison of a crossover operator in binary coded genetic algorithms, WSEAS Transaction. on Computers, vol.9, n.9, pp. 1064– 1073, 2010.

D. Kusum, T. Manoj, A new crossover operator for real coded genetic algorithms, Applied Mathematics and Computation, vol.188, pp.895–911, 2007.

A. Ferrolho, M. Crisostomo, Genetic Algorithms: concepts, techniques and applications, WSEAS Transactions on Advances in Engineering Education, Vol. 2, pp. 12–19,2005.

C. M, Garci, M. Lozano, F. Herrera, D. Molina, A.M. Sa´nchez, Global and local real-coded genetic algorithms based on parent centric crossover operators, European Journal of Operational Research, vol. 185, pp. 1088–1113, 2008.

Jazebi, S., Jazebi, S., Rashidinejad, M., Application of a novel real genetic algorithm to accelerate the distribution network reconfiguration, (2009) International Review of Electrical Engineering (IREE), 4 (1), pp. 114-121.

L. Booker, Improving search in genetic algorithms, In Genetic Algorithms and Simulated Annealing, L. Davis (Ed.). Morgan Kaufmann Publishers, 1987.

M. Kaya, The effects of two new crossover operators on genetic algorithm performance, Applied Soft Computing, vol.11, issue 1, pp. 881–890, 2011.

C. M. Garci, M. Lozano, F.Herrera, D. Molina, A, M. Sa´Nchez, Global And Local Real-Coded Genetic Algorithms Based On Parent Centric Crossover Operators, European Journal Of Operational Research,Vol.185,pp.1088–1113,2008.


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