International Journal of Man-Machine Studies
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
A review of feature selection techniques in bioinformatics
Bioinformatics
AMT'10 Proceedings of the 6th international conference on Active media technology
Using feature selection approaches to find the dependent features
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Efficient EDA for large opimization problems via constraining the search space of models
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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The emergence of complex adaptive traits and behaviors in artificial life systems requires long term evolution with continuous emergence governed by natural selection. We model organism's genomes in an individual-based evolutionary ecosystem simulation (EcoSim), with fuzzy cognitive maps (FCM) representing their behavioral traits. Our system allows for the emergence of new traits and disappearing of others, throughout a course of evolution. We show how EcoSim models evolution through the behavioral model of its individuals governed by natural selection. We validate our model by examining the effect, the emergence of new genes, has on individual's fitness. Machine learning tools showed great interest lately in modern biology, evolutionary genetics and bioinformatics domains. We use Random Forest classifier, which has been widely used lately due to its power of dealing with large number of attributes with high efficiency, to predict fitness value knowing only the values of new genes. Furthermore discovering meaningful rules behind the fitness prediction encouraged us to use a pre processing step of feature selection. The selected features were then used to deduce important rules using the JRip learner algorithm.