Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Self-Organizing Maps
A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms
Evolutionary Computation
Multistrategy self-organizing map learning for classification problems
Computational Intelligence and Neuroscience
HEp-2 cell images classification based on textural and statistic features using self-organizing map
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
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This paper describes self-organizing maps for genetic algorithm (SOM-GA) which is the combinational algorithm of a real-coded genetic algorithm (RCGA) and self-organizing map (SOM). The self-organizing maps are trained with the information of the individuals in the population. Sub-populations are defined by the help of the trained map. The RCGA is performed in the sub-populations. The use of the sub-population search algorithm improves the local search performance of the RCGA. The search performance is compared with the real-coded genetic algorithm (RCGA) in three test functions. The results show that SOM-GA can find better solutions in shorter CPU time than RCGA. Although the computational cost for training SOM is expensive, the results show that the convergence speed of SOM-GA is accelerated according to the development of SOM training.