Using promoters and functional introns in genetic algorithms for neuroevolutionary learning in non-stationary problems

Abstract

This paper addresses the problem of adaptive learning in non-stationary problems through neuroevolution. It is a general problem that is very relevant in many tasks, for example, in the context of robot model learning from interaction with the world. Traditional learning algorithms fail in this task as they have mostly been designed for learning a single model in a static setting. Neuroevolutionary techniques have obtained promising results in this non-stationary context but are still lacking in certain types of problems, especially those dealing with information streams where different portions correspond to different models. An extension through the introduction of the concept of introns and promoter genes enables neuroevolutionary algorithms to improve their performance on this type of problems. Following this approach, an implementation of these concepts on a genetic algorithm for neuroevolution is presented here. This algorithm is called promoter based genetic algorithm (PBGA) and it uses a genotypic representation with a set of features that allows for an intrinsic memory in the population that is self-regulated, in the sense that functional parts of the individuals are preserved through generations without an explicit knowledge about the number of different tasks or models that have to arise from the data stream. Some illustrative tests of the potential of these techniques based on the continuous switch between completely different objective functions that must be learnt are presented and the results are analyzed and compared to other neuroevolutionary algorithms.