47 citations · 150 across the 18 of their papers we have counts for
19 papers
Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation
Du Nguyen Duy, Michael Affenzeller, Ramin-Nikzad Langerodi
Symbolic Regression is a powerful data-driven technique that searches for mathematical expressions that explain the relationship between input variables and a target of interest. D…
A Probabilistic Transformation of Distance-Based Outliers
David Muhr, Michael Affenzeller, Josef Küng
The scores of distance-based outlier detection methods are difficult to interpret, making it challenging to determine a cut-off threshold between normal and outlier data points wit…
Vectorial Genetic Programming -- Optimizing Segments for Feature Extraction
Philipp Fleck, Stephan Winkler, Michael Kommenda +1
Vectorial Genetic Programming (Vec-GP) extends GP by allowing vectors as input features along regular, scalar features, using them by applying arithmetic operations component-wise…
OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language
David Muhr, Michael Affenzeller, Anthony D. Blaom
OutlierDetection.jl is an open-source ecosystem for outlier detection in Julia. It provides a range of high-performance outlier detection algorithms implemented directly in Julia.…
A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement
Kaifeng Yang, Guozhi Dong, Michael Affenzeller
Bayesian global optimization (BGO) is an efficient surrogate-assisted technique for problems involving expensive evaluations. A parallel technique can be used to parallelly evaluat…
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
Bogdan Burlacu, Michael Kommenda, Gabriel Kronberger +2
Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle sim…
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
Hao Wang, Kaifeng Yang, Michael Affenzeller
Hypervolume improvement (HVI) is commonly employed in multi-objective Bayesian optimization algorithms to define acquisition functions due to its Pareto-compliant property. Rather…
Cluster Analysis of a Symbolic Regression Search Space
Gabriel Kronberger, Lukas Kammerer, Bogdan Burlacu +3
In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space. The motivation for this work is to imp…
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu +3
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available. Such scenarios often require spe…
Optimization Networks for Integrated Machine Learning
Michael Kommenda, Johannes Karder, Andreas Beham +4
Optimization networks are a new methodology for holistically solving interrelated problems that have been developed with combinatorial optimization problems in mind. In this contri…
Complexity Measures for Multi-objective Symbolic Regression
Michael Kommenda, Andreas Beham, Michael Affenzeller +1
Multi-objective symbolic regression has the advantage that while the accuracy of the learned models is maximized, the complexity is automatically adapted and need not be specified…
On the Effectiveness of Genetic Operations in Symbolic Regression
Bogdan Burlacu, Michael Affenzeller, Michael Kommenda
This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the…
Concept Drift Detection with Variable Interaction Networks
Jan Zenisek, Gabriel Kronberger, Josef Wolfartsberger +2
The current development of today's production industry towards seamless sensor-based monitoring is paving the way for concepts such as Predictive Maintenance. By this means, the co…
Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression
Bogdan Burlacu, Lukas Kammerer, Michael Affenzeller +1
We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symb…
Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure
Bogdan Burlacu, Michael Affenzeller, Gabriel Kronberger +1
Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires…
Resource-constrained multi-project scheduling with activity and time flexibility
Viktoria A. Hauder, Andreas Beham, Sebastian Raggl +2
Project scheduling in manufacturing environments often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simul…
Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset
Michael Kommenda, Gabriel Kronberger, Christoph Feilmayr +1
In this paper a data mining approach for variable selection and knowledge extraction from datasets is presented. The approach is based on unguided symbolic regression (every variab…
On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection
Gabriel Kronberger, Stephan Winkler, Michael Affenzeller +2
Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve among others regression, classification, and time-series…
Macro-Economic Time Series Modeling and Interaction Networks
Gabriel Kronberger, Stefan Fink, Michael Kommenda +1
Macro-economic models describe the dynamics of economic quantities. The estimations and forecasts produced by such models play a substantial role for financial and political decisi…