Abstract
This paper proposes a hardware realization of the crossover module in the genetic algorithm for the travelling salesman problem (TSP). In order to enhance performance, we employ a combination of pipelining and parallelization with a genetic algorithm (GA) processor to improve processing speed, as compared to software implementation. Simulation results showed that the proposed architecture is six times faster than the similar existing architecture. The presented field-programmable gate array (FPGA) implementation of PMX crossover operator is more than 400 times faster than in software.
Highlights
genetic algorithm (GA) starts from the initial population of randomly generated individuals, each of which is an encoding of a solution to the problem
In order to get some ideas about the hardware implementation of crossover, we have investigated some different crossover architectures
Since most of the existing hardware implementations are complete GA algorithm [9,11,12], they reported the quality of solutions and the search speed as performance metrics, so, there is no crossover performance evaluation individually and independently
Summary
Genetic algorithm (GA) is a robust stochastic optimization technique that is based on the principle of survival of the fittest in nature. The GA has an ability to provide optimum solution to a wide range of problems. GA starts from the initial population of randomly generated individuals, each of which is an encoding of a solution to the problem. The overall rule for how to select parents from the population for reproduction is survival of the fittest. Individuals are chosen to mate with probability proportional to their fitness. GA evolves the population over generations using crossover and mutation operations
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