Abstract
We present a Genetic Algorithm (GA) capable of optimizing two different applications. Everything (except elitism operator) is the same in both applications, including the values of GA parameters. Whereas the two applications are very different: One of them presents a deterministic behavior during the GA iterations, and the other one presents a stochastic behavior. For this different nature of the applications, a new approach to elitism operator is presented. It provides a more efficient and robust solution. For each application, the efficiency of the optimization process performed by GA is demonstrated by comparison of the results with another classical methods’ output. At the same time, our GA presents some characteristics as robustness, convergence to solution, extraordinary capability of generalization and an easiness of being coded for parallel processing architectures, that make our GA very suitable for multiple optimization processes, independently if they are of a deterministic nature or a stochastic one.
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Seijas, J., Morató, C., Sanz-González, J.L. (2002). Genetic Algorithms: Two Different Elitism Operators for Stochastic and Deterministic Applications. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2001. Lecture Notes in Computer Science, vol 2328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48086-2_68
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DOI: https://doi.org/10.1007/3-540-48086-2_68
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