Comparison of Hsclone and Roulette Genetic Algorithms on the Application of Combinational Circuits
Suhas S1, Gayatri Malhotra2, Rajini V.H3
1Suhas S, Student, Department of Electrical Communication Engineering, RNS Institute of Technology, Bangalore (Karnataka), India.
2Gayatri Malhotra, Scientist/Engineer, ISRO Satellite Centre, Bangalore (Karnataka), India.
2Dr. Rajini V.H, Associate Professor, Department of Electrical Communication Engineering, RNS Institute of Technology, Bangalore (Karnataka), India.
Manuscript received on 15 April 2016 | Revised Manuscript received on 25 April 2016 | Manuscript Published on 30 April 2016 | PP: 170-176 | Volume-5 Issue-4, April 2016 | Retrieval Number: D4527045416/16©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Future planetary and deep space exploration require robust methods of operation to operate spacecraft in the outer atmosphere without any variations or faults. The best fault tolerant method which can be used for operations of this kind is the class of Genetic Algorithms (GA) which are a sort of evolutionary algorithm. In this domain of operation, a combinational circuit is designed by the method of Cartesian Genetic Programming (CGP). The Circuit after the design is fed to the two GAs, namely, HsClone and Roulette. The main advantage in this use of GA is the likely determination of the best possible circuit within the space of a thousand circuits. The combinational circuit design is applied to both the algorithms and tested for fitness. After the required fitness is obtained, both the algorithms are compared with respect to their cumulative generational fitness and other allied aspects. The better algorithm will hence be determined to integrate it into the future design of spacecraft hardware. This is expected to help the spacecraft recover from Single Event Upsets (SEU) which usually occur due to hostile temperature conditions and outer atmospheric radiation.
Keywords: Cartesian Genetic Programming (CGP), Genetic Algorithm (GA), Evolvable Hardware (EHW), Reconfigurable FPGA, Evolutionary Algorithm (EA)
Scope of the Article: Algorithm Engineering