Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
Stochastic Hillclimbing as a Baseline Method for
Stochastic Hillclimbing as a Baseline Method for
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Learning Membership Functions in Takagi-Sugeno Fuzzy Systems by Genetic Algorithms
ACIIDS '09 Proceedings of the 2009 First Asian Conference on Intelligent Information and Database Systems
Simplifying Particle Swarm Optimization
Applied Soft Computing
Feed-forward artificial neural network based inference system applied in bioinfonnatics data-mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Dynamic penalty based GA for inducing fuzzy inference systems
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Bioinformatics integration framework for metabolic pathway data-mining
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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This paper describes a novel meta-learning (MTL) based methodology used to optimize a neural network based inference system. The inference system being optimized is part of a bioinformatic application built to implement a systematic search scheme for the identification of genes which encode enzymes of metabolic pathways. Different MTL implementations are contrasted with manually optimized inference systems. The MTL based approach was found to be flexible and able to produce better results than manual optimization.