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A novel adaptive non-linear mapping (ANLM), integrating an adaptive mapping error (AME) with a chaos-genetic algorithm (CGA) including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping (NLM). The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodically in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results demonstrated that the whole performance of CGA is better than that of traditional genetic algorithm. Furthermore, a typical example of mapping eight-dimensional olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM.
A novel adaptive non-linear mapping (ANLM), integrating an adaptive mapping error (AME) with a chaos-genetic algorithm (CGA) including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping (NLM). The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square two tow is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodically in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results in that the whole performance of CGA is better than that of traditional genetic algorithms. Furthermore, a typical example of mapping eight-dimensional olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM.