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Local Optimization and Complexity Control for Symbolic Regression
Michael Kommenda
Research Center Hagenberg
HEAL
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Doctoral Thesis
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Dive into the research topics of 'Local Optimization and Complexity Control for Symbolic Regression'. Together they form a unique fingerprint.
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Keyphrases
Symbolic Regression
100%
Prediction Accuracy
100%
Local Optimization
100%
Local Complexity
100%
Complexity Control
100%
Control Optimization
100%
Genetic Programming
50%
Complexity Measures
33%
Bloating
33%
Appropriate Model
16%
Model Complexity
16%
Semantic Information
16%
Optimization Methods
16%
Data-centric
16%
Nonlinear Least Squares
16%
Pareto Front
16%
Single Solution
16%
Interpretable Prediction Model
16%
Machine Learning Approach
16%
Mathematical Expression
16%
Simultaneous Optimization
16%
Numerical Model
16%
Syntactic Information
16%
Model Parameter Tuning
16%
Curve Model
16%
Marquardt Algorithm
16%
Single-objective
16%
Multi-objective Symbolic Regression
16%
Computer Science
Prediction Accuracy
100%
Local Optimization
100%
Structure Model
66%
Genetic Programming
50%
Multiobjective
33%
Complexity Measure
33%
Regression Problem
33%
Interpretability
33%
Single Objective
16%
Model Complexity
16%
Metaheuristics
16%
Machine Learning-Based Approach
16%
Least Squares Method
16%
Single Solution
16%
Prediction Model
16%
Syntactics
16%
Optimization Technique
16%
Fitting Curve
16%
Mathematics
Model Structure
100%
Interpretability
50%
Marquardt Algorithm
25%
Subproblem
25%
Least Squares Method
25%
Curve Fitting
25%