Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
The Strength of Weak Learnability
Machine Learning
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Original Contribution: Stacked generalization
Neural Networks
Self-organization of nets of active neurons
Systems Analysis Modelling Simulation
Machine Learning
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
Machine Learning
Arbitrating among competing classifiers using learned referees
Knowledge and Information Systems
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Journal of Global Optimization
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
A Comparison of Parallel and Sequential Niching Methods
Proceedings of the 6th International Conference on Genetic Algorithms
MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
EPNet for Chaotic Time-Series Prediction
SEAL'96 Selected papers from the First Asia-Pacific Conference on Simulated Evolution and Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
An Easily Calculated Bound on Condition for Orthogonal Algorithms
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Optimizing synaptic weights of neural networks
ICECT'03 Proceedings of the third international conference on Engineering computational technology
Making the most of what you've got: using models and data to improve prediction accuracy
Making the most of what you've got: using models and data to improve prediction accuracy
Learning evaluation functions for global optimization
Learning evaluation functions for global optimization
Boosting stochastic problem solvers through online self-analysis of performance
Boosting stochastic problem solvers through online self-analysis of performance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Advances in Engineering Software
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Co-evolving recurrent neurons learn deep memory POMDPs
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Case-based heuristic selection for timetabling problems
Journal of Scheduling
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Evolving evolutionary algorithms using evolutionary algorithms
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A novel generative encoding for exploiting neural network sensor and output geometry
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Free lunches for function and program induction
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Free lunches for neural network search
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Handbook of Metaheuristics
A comparison of particle swarm optimization algorithms based on run-length distributions
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
MAGMA: a multiagent architecture for metaheuristics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Computers & Mathematics with Applications
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Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply meta-learning principles to the neural network structure and function optimization. We show that diversity promotion, ensembling, self-organization and induction are beneficial for the problem. We combine several different neuron types trained by various optimization algorithms to build a supervised feed-forward neural network called Group of Adaptive Models Evolution (GAME). The approach was tested on a large number of benchmark data sets. The experiments show that the combination of different optimization algorithms in the network is the best choice when the performance is averaged over several real-world problems.