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Effects of Diversity on Optimality in GA

  • Conference paper
Artificial Intelligence and Computational Intelligence (AICI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5855))

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Abstract

Genetic Algorithms (GA) is an evolutionary inspired heuristic search algorithm. Like all heuristic search methods, the probability of locating the optimal solution is not unity. Therefore, this reduces GA’s usefulness in areas that require reliable and accurate optimal solutions, such as in system modeling and control gain setting. In this paper an alteration to Genetic Algorithms (GA) is presented. This method is designed to create a specific type of diversity in order to obtain more optimal results. In particular, it is done by mutating bits that are not constant within the population. The resultant diversity and final optimality for this method is compared with standard Mutation at various probabilities. Simulation results show that this method improves search optimality for certain types of problems.

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© 2009 Springer-Verlag Berlin Heidelberg

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MacDonald, G., Fang, G. (2009). Effects of Diversity on Optimality in GA. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_15

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  • DOI: https://doi.org/10.1007/978-3-642-05253-8_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05252-1

  • Online ISBN: 978-3-642-05253-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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