Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
PRIB'06 Proceedings of the 2006 international conference on Pattern Recognition in Bioinformatics
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Hi-index | 0.00 |
Computational modeling of biological neurons allows for exploration of many parameter combinations and various types of neuronal activity, without requiring a prohibitively large number of "wet" experiments. On the other hand, analysis and biological interpretation of such, often very extensive, databases of models can be difficult. In this article, we present two Computational Intelligence (CI) approaches, based on Artificial Neural Networks (ANN) and Multi-Objective Evolutionary Algorithms (MOEA), that we have successfully applied to the problem of analysis and interpretation of model neuronal data.