Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Improving the fuzzy system performance by fuzzy system ensemble
Fuzzy Sets and Systems
A GA-based fuzzy adaptive learning control network
Fuzzy Sets and Systems
Comparative evaluation of genetic algorithm and backpropagation for training neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Combining GP operators with SA search to evolve fuzzy rule based classifiers
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Pareto Simulated Annealing for Fuzzy Multi-Objective Combinatorial Optimization
Journal of Heuristics
Crossover, Macromutationand, and Population-Based Search
Proceedings of the 6th International Conference on Genetic Algorithms
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Graph Based GP Applied to Dynamical Systems Modeling
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Chaos and Time-Series Analysis
Chaos and Time-Series Analysis
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Nonlinear model for ECG R-R interval variation using genetic programming approach
Future Generation Computer Systems
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Expert Systems with Applications: An International Journal
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
An observer-based approach to controlling time-delay chaotic systems via Takagi-Sugeno fuzzy model
Information Sciences: an International Journal
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Minimizing Energy Consumption in Heating Systems under Uncertainty
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
International Journal of Approximate Reasoning
A Thermodynamical Model Study for an Energy Saving Algorithm
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization
Information Sciences: an International Journal
Information Sciences: an International Journal
Multiobjective memetic algorithms for time and space assembly line balancing
Engineering Applications of Artificial Intelligence
Comparison of fuzzy functions for low quality data GAP algorithms
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A multiobjective evolutionary programming framework for graph-based data mining
Information Sciences: an International Journal
Evolutionary multi-objective optimization for mesh simplification of 3D open models
Integrated Computer-Aided Engineering
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Transparent models search for a balance between interpretability and accuracy. This paper is about the estimation of transparent models of chaotic systems from data, which are accurate and simple enough for their expression to be understandable by a human expert. The models we propose are discrete, built upon common blocks in control engineering (gain, delay, sum, etc.) and optimized both in their complexity and accuracy. The accuracy of a discrete model can be measured by means of the average error between its prediction for the next sampling period and the true output at that time, or 'one-step error'. A perfect model has zero one-step error, but a small error is not always associated with an approximate model, especially in chaotic systems. In chaos, an arbitrarily low difference between two initial states will produce uncorrelated trajectories, thus a model with a low one-step error may be very different from the desired one. Even though a recursive evaluation (multi-step prediction) improves the fitting, in this work we will show that a learning algorithm may not converge to an appropriate model, unless we include some terms that depend on estimates of certain properties of the model (so called 'invariants' of the chaotic series). We will show this graphically, by means of the reconstructed attractors of the original system and the model. Therefore, we also propose to follow a multi-objective approach to model chaotic processes and to apply a simulated annealing-based optimization to obtain transparent models.