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
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
Evolutionary Approach to Non-stationary Optimisation Tasks
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Non-stationary function optimization using polygenic inheritance
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Introducing long term memory in an ANN based multilevel darwinist brain
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
| Hi-index | 0.00 |
This paper deals with the topic of learning through neuroevolutionary algorithms in non-stationary settings. This kind of algorithms that evolve the parameters and/or the topology of a population of Artificial Neural Networks have provided successful results in optimization problems in stationary settings. Their application to non-stationary problems, that is, problems that involve changes in the objective function, still requires more research. In this paper we address the problem through the integration of implicit, internal or genotypic, memory structures and external explicit memories in an algorithm called Promoter Based Genetic Algorithm with External Memory (PBGA-EM). The capabilities introduced in a simple genetic algorithm by these two elements are shown on different tests where the objective function of a problem is changed in an unpredictable manner.