Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Combining GP operators with SA search to evolve fuzzy rule based classifiers
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
The GA-P: A Genetic Algorithm and Genetic Programming Hybrid
IEEE Expert: Intelligent Systems and Their Applications
A Metric for Genetic Programs and Fitness Sharing
Proceedings of the European Conference on Genetic Programming
Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Higher order models for fuzzy random variables
Fuzzy Sets and Systems
Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms
Information Sciences: an International Journal
International Journal of Approximate Reasoning
Taximeter verification using imprecise data from GPS
Engineering Applications of Artificial Intelligence
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
An study of the tree generation algorithms in equation based model learning with low quality data
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Interval-valued GA-P algorithms
IEEE Transactions on Evolutionary Computation
A method for ranking fuzzy numbers and its application to decision-making
IEEE Transactions on Fuzzy Systems
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The undesired effects of data gathered from real world can be produced by the noise in the process, the bias of the sensors and the presence of hysteresis, among other uncertainty sources. Data gathered by this way are called Low Quality Data (LQD). Thus, uncertainty representation tools are needed for using in learning models with this kind of data. This work presents a method to represent the uncertainty and an approach for learning white box Equation Based Models (EBM). The proficiency of the representations with different noise levels and fitness functions typology is compared. The numerical results show that the use of the described objectives improves the proficiency of the algorithms. It has been also proved that each meta-heuristic determines the typology of fitness function.