Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Giri:2013:ASC,

• author = "Brijesh Kumar Giri and Jussi Hakanen and Kaisa Miettinen and Nirupam Chakraborti",
• title = "Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives",
• journal = "Applied Soft Computing",
• year = "2013",
• volume = "13",
• number = "5",
• pages = "2613--2623",
• month = may,
• keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Neural networks, ANN, Multi-objective optimisation, MOGP, Computational cost, Meta-models, Simulation-based optimisation",
• ISSN = "1568-4946",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494612005091",
• DOI = "doi:10.1016/j.asoc.2012.11.025",
• size = "11 pages",
• abstract = "A new bi-objective genetic programming (BioGP) technique has been developed for meta-modelling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimises training error through a single objective optimisation procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat, a perennial problem in genetic programming along with over fitting and under fitting problems. In this study the meta-models constructed for SMB reactors were compared with those obtained from an evolutionary neural network (EvoNN) developed earlier and also with a polynomial regression model. Both BioGP and EvoNN were compared for subsequent constrained bi-objective optimization studies for the SMB reactor involving four objectives. The results were also compared with the previous work in the literature. The BioGP technique produced acceptable results and is now ready for data-driven modelling and optimization studies at large.",

}

Genetic Programming entries for Brijesh Kumar Giri Jussi Hakanen Kaisa Miettinen Nirupam Chakraborti

Citations