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SOMA — Self-Organizing Migrating Algorithm

  • Ivan Zelinka
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 141)

Abstract

In recent years, a broad class of algorithms has been developed for stochastic optimization, i.e. for optimizing systems where the functional relationship between the independent input variables x and output (objective function) y of a system S is not known. Using stochastic optimization algorithms such as Genetic Algorithms (GA), Simulated Annealing (SA) and Differential Evolution (DE), a system is confronted with a random input vector and its response is measured. This response is then used by the algorithm to tune the input vector in such a way that the system produces the desired output or target value in an iterative process.

Keywords

Cost Function Search Space Differential Evolution Memetic Algorithm Black Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ivan Zelinka

There are no affiliations available

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