Genetic_Programming_Theory_and_Practice_XIII
Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming Michael Kommenda, Gabriel Kronberger, Michael ...
2 M. Kommenda et al. the necessary model structure to describe the data implicitly. Another benefit due to the model being descr ...
Evolving Simple Symbolic Regression Models 3 trees. Another proposed complexity measure is the number of variable symbols (eithe ...
4 M. Kommenda et al. containing lots of constants. As a result the constant symbol could gain prevalence in the trees of the pop ...
Evolving Simple Symbolic Regression Models 5 the Pearson’s correlation coefficientR^2 ), but in addition to the population a sep ...
6 M. Kommenda et al. ab Fig. 1 Comparison of the development of symbolic expression tree length over generations for standard an ...
Evolving Simple Symbolic Regression Models 7 Standard Discrete 0 20 40 60 80 Number of Models in the Pareto front Fig. 2 Number ...
8 M. Kommenda et al. Fig. 3 Exemplary Pareto fronts generate either by an NSGA-II using the standard or the discretized objectiv ...
Evolving Simple Symbolic Regression Models 9 Ta b l e 1 Algorithm settings for the performed experiments (multiple values indica ...
10 M. Kommenda et al. Ta b l e 2 Description of artificial and real-world problems and the training and test ranges Name Functio ...
Evolving Simple Symbolic Regression Models 11 Ta b l e 3 Performance of the best models of each algorithm variant in terms of th ...
12 M. Kommenda et al. The length of the evolved symbolic regression models for all single-objective genetic programming configur ...
Evolving Simple Symbolic Regression Models 13 Ta b l e 4 Analysis of the used functions in the best models in terms of the subtr ...
14 M. Kommenda et al. Ta b l e 5 Size statistics of the best models for Problem-2 per algorithm variant Original model Simplifie ...
Evolving Simple Symbolic Regression Models 15 4.2.2 Noisy Data The same algorithm settings as in the previous experiments have b ...
16 M. Kommenda et al. Ta b l e 6 Performance of the best models of each algorithm variant in terms of the NMSE on the training a ...
Evolving Simple Symbolic Regression Models 17 Ta b l e 7 Analysis of the functions in the best models in terms of the subtree si ...
18 M. Kommenda et al. objective genetic programming has been performed by utilizing NSGA-II with slight adaptations to make it s ...
Evolving Simple Symbolic Regression Models 19 Luke S, Panait L (2002) Lexicographic Parsimony Pressure. In: Langdon WB, Cantú-Pa ...
Learning Heuristics for Mining RNA Sequence-Structure Motifs Achiya Elyasaf, Pavel Vaks, Nimrod Milo, Moshe Sipper, and Michal Z ...
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