Reconstructing Dynamic Target Functions by Means of Genetic Programming Using Variable Population Size

Dynamic environments are becoming more and more popular in many applicative domains. A large amount of literature has appeared to date dealing with the problem of tracking the extrema of dynamically changing target functions, but relatively few material has been produced on the problem of reconstructing the shape, or more generally finding the equation, of dynamically changing target functions. Nevertheless, in many applicative domains, reaching this goal would have an extremely important impact. It is the case, for instance, of complex systems modelling, like for instance biological systems or systems of biochemical reactions, where one is generally interested in understanding what's going on in the system over time, rather than following the extrema of some target functions. Last but not least, we also believe that being able to reach this goal would help researchers to have a useful insight on the reasons that cause the change in the system over time, or at least the pattern of this modification. This paper is intended as a first preliminary step in the attempt to fill this gap. We show that genetic programming with variable population size is able to adapt to the environment modifications much faster (i.e. using a noteworthy smaller amount of computational effort) than standard genetic programming using fixed population size. The suitability of this model is tested on a set of benchmarks based on some well known symbolic regression problems.