Journal of Computer Science and Information Security IJCSIS March 2013 by ijcsis

Author:ijcsis
Language: eng
Format: epub
Tags: IJCSIS; call for paper; journal; computer science; research; google scholar; IEEE; Scirus; download; ArXiV; library; information security; internet; peer review; scribd; docstoc; cornell university; archive; Journal of Computing; DOAJ; Open Access; March 2013; Volume 11; No. 3; Impact Factor; engineering; international; proQuest; computing; computer; technology

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Fig. 2: Line graphs interpretation of Boolean 6-multiplexer

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Fig. 3: line graphs interpretation of Boolean 11-multiplexer

VI. DISCUSSION AND FINDINGS

When the Boolean 6-multiplexer function and Boolean 11-Multiplexer function were executed against the preparatory requirements using our proposed algorithm, very impressive results were obtained. In the Boolean 6-multipler function, at various interval of run, the program size increased steadily as evolution progresses indicating a sign of code bloat, but after certain level of run, the program size dropped considerably as program fitness increases. The Boolean 11-multiplexer function also displayed almost the same behaviour; except that it exhibited better performance than the Boolean 6-multiplexer result in terms of better program size reduction. To this extent, it performed better in bloat control. This is in line with the 'No-free lunch' theorem that states that there is no single algorithm that provides omnibus solution to all problems.

VII. CONCLUSIONS

This paper Proposed a new method of code bloat control algorithm known as the DK-PC algorithm that Delete lower and Keep higher Fitness value Programs after Crossover. This algorithm was tested against the Boolean 6-multiplexer and Boolean 11-mutiplexer functions, and we observed that the algorithm was able to control bloat to a large degree. However, the algorithm performed better in the Boolean 11-multiplexer function than in the Boolean 6-multiolexer function. It is therefore clear to us that bloat is inevitable, but what extent can it be control? Our algorithm has considerably reduced code bloat to the barest minimum. Our advice for further research is that, other benchmark problems like artificial ant problem, symbolic regression problems and so on can be used to further test the efficacy of this algorithm.

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http://sites.google.com/site/ijcsis/ ISSN 1947-5500

(IJCSIS) International Journal of Computer Science and Information Security, Vol. 11, No. 3, March 2013

REFERENCES

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[4] D, S. Burke, K. A. De Jong, J. J. Grefenstette, R. Crawford-

Marks, and L. Spector (2002), "Size control via size fair [24]

genetic operators in the pushGP genetic programming system," Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2002, San-Francisco, CA: Morgan Kaufmann. [25]

[5] K. Chellapilla (1997), "Evolving computer programs without

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[6] C. Darwin (1996), " The origin of species. Oxford, UK: Oxford

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[7] E. D. de Jong, R. Watson, and J. Pollack

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