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. 1. INTRODUCTION Conventional genetic programming (GP), and evolutionary algorithms (EA), in general, assign a single performance measure to each individual based on the evaluation of a scalar fitness function. However, these population-based methods possess the characteristic of simultaneously searching for multiple solut..