Co-evolution of non-linear PLS model components

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@Article{Searson:2007:JC,

• author = "Dominic Searson and Mark Willis and Gary Montague",
• title = "Co-evolution of non-linear {PLS} model components",
• journal = "Journal of Chemometrics",
• year = "2007",
• volume = "21",
• number = "12",
• pages = "592--603",
• month = dec,
• keywords = "genetic algorithms, genetic programming, partial least squares, symbolic regression, evolutionary computation, co-operative co-evolution",
• ISSN = "1099-128X",
• ISSN = "0886-9383",
• DOI = "doi:10.1002/cem.1084",
• abstract = "The issue of outer model weight updating is important in extending partial least squares (PLS) regression to modelling data that shows significant non-linearity. This paper presents a novel co-evolutionary component approach to the weight updating problem. Specification of the non-linear PLS model is achieved using an evolutionary computational (EC) method that can co-evolve all non-linear inner models and all input projection weights simultaneously. In this method, modular symbolic non-linear equations are used to represent the inner models and binary sequences are used to represent the projection weights. The approach is flexible, and other representations could be employed within the same co-evolutionary framework. The potential of these methods is illustrated using a simulated pH neutralisation process data set exhibiting significant non-linearity. It is demonstrated that the co-evolutionary component architecture can produce results which are competitive with non-linear neural network-based PLS algorithms that use iterative projection weight updating. In addition, a data sampling method for mitigating overfitting to the training data is described",

}

Genetic Programming entries for Dominic Patrick Searson Mark J Willis Gary A Montague

Citations