Abstract
The paper deals with an evolutionary algorithm which uses new methods for controlling the range of mutation. In order to significantly increase the efficiency in finding the optimum, it discovers and exploits knowledge about the state of population in the environment in the every generation. It allows to find the solution to be found both quickly and accurately. By dividing the population into objects dealing with different functions of optimization, it can simultaneously explore as well as exploit the solutions space. These abilities allow to increase the algorithm efficiency also in multi-dimensional environments.
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Wawrzyniak, D., Obuchowicz, A. (2008). Evolutionary Algorithm with Forced Variation in Multi-dimensional Non-stationary Environment. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2007. Lecture Notes in Computer Science, vol 4967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68111-3_62
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DOI: https://doi.org/10.1007/978-3-540-68111-3_62
Publisher Name: Springer, Berlin, Heidelberg
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