Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Automated neuron model optimization techniques: a review
Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
Mining probabilistic models learned by EDAs in the optimization of multi-objective problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental data and this question can be posed as an optimization problem. In this paper we investigate the behavior of different estimation of distribution algorithms (EDAs) for this problem. We focus on studying the influence that the interactions between the neuron model conductances have in the complexity of the optimization problem. We support evidence that the use of these interactions during the optimization process can improve the EDA behavior.