Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Symbiotic evolution of neural networks in sequential decision tasks
Symbiotic evolution of neural networks in sequential decision tasks
Evolving neural networks through augmenting topologies
Evolutionary Computation
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Solving Non-Markovian Control Tasks with Neuro-Evolution
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Evolutionary Approach to Non-stationary Optimisation Tasks
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Non-stationary function optimization using polygenic inheritance
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
Influence of promoter length on network convergence in GRN-based evolutionary algorithms
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
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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.