Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
International Journal of Approximate Reasoning
A fuzzy logic based efficient energy saving approach for domestic heating systems
Integrated Computer-Aided Engineering
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Taximeter verification with GPS and soft computing techniques
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A soft computing method for detecting lifetime building thermal insulation failures
Integrated Computer-Aided Engineering
Neural visualization of network traffic data for intrusion detection
Applied Soft Computing
Analysing the low quality of the data in lighting control systems
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
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
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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. In previous works the learning models using the so-called Low Quality Data (LQD) has been studied in order to analyze the way to represent the uncertainty. It makes use of genetic programming and the multiobjective simmulated annealing heuristic, which has been hybridized with genetic operators. The role of the tree generation methods when learning LQD was studied in that paper. The present work deals with the analysis of the generation methods relevance in depth and provides with statistical studies on the obtained results.