Multiobjective Genetic Programming: a Nonlinear System Identification Application

Genetic programming (GP) is applied to a multobjective optimisation problem and the advantages of its hierarchical tree encoding scheme are compared with an earlier use of a subset representation approach which used string-encoded genetic algorithms. The GP approach is applied to the identification of non-linear system polynomial models and provides a trade-off between the complexity and the performance of the models. Multiobjective Genetic Programming is not only presented as a tool for optimising multiple objectives of the problem under investigation, but also provides a means of controlling the size of tree structures used in the search process.