Abstract
The research proposes a selfish gene image segmentation algorithm as an alternative to Genetic Algorithm. Research in Genetic Algorithms originated from Darwin’s theory faced the problem of finding the optimal solution due to its inherent characteristic of genetic drift and premature convergence. Selfish gene views genes as the basic unit in evolution. Thus the color image segmentation algorithm is designed based on virtual population with collection of genes rather than fixed genes chromosomes. The genes are positioned into predetermined loci forming two chromosomes that make up the virtual population in each generation. The chromosomes are rewarded and penalized according to the chromosomes performance. Evaluation with the ground truth images shows that the selfish gene is able to detect the variation of colors very similar to the way eye detect color.
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Khalid, N.E.A., Ariff, N.M., Fadzil, A.F.A., Noor, N.M. (2015). Selfish Gene Image Segmentation Algorithm. In: Berry, M., Mohamed, A., Yap, B. (eds) Soft Computing in Data Science. SCDS 2015. Communications in Computer and Information Science, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-287-936-3_12
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DOI: https://doi.org/10.1007/978-981-287-936-3_12
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