Multiobjective GP for Human-Understandable Models: A Practical Application

The work presented in this chapter is concerned with the identification and modelling of nonlinear dynamical systems using multiobjective evolutionary algorithms (MOEAs). This problem involves the processes of structure selection, parameter estimation, model performance and model validation and defines a complex solution space. Evolutionary algorithms (EAs), in particular genetic programming (GP), are found to provide a way of evolving models to solve this identification and modelling problem, and their use is extended to encompass multiobjective functions. Multiobjective genetic programming (MOGP) is then applied to multiple conflicting objectives in order to yield a set of simple and valid human-understandable models which can reproduce the behaviour of a given unknown system.

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