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Found 19 papers, 3 papers with code
Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation
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.
A Probabilistic Transformation of Distance-Based Outliers
We describe a generic transformation of distance-based outlier scores into interpretable, probabilistic estimates.
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Vectorial Genetic Programming -- Optimizing Segments for Feature Extraction
The presented results indicate, that the different random sampling strategies do not impact the overall algorithm performance significantly, and that the guided strategies suffer from becoming stuck in local optima.
OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language
Additionally, it provides a standardized, yet flexible, interface for future outlier detection algorithms and allows for model composition unseen in previous packages.
A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement
This paper proposes five alternatives of \emph{Probability of Improvement} (PoI) with multiple points in a batch (q-PoI) for multi-objective Bayesian global optimization (MOBGO), taking the covariance among multiple points into account.
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results.
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
This work provides the exact expression of the probability distribution of the hypervolume improvement (HVI) for bi-objective generalization of Bayesian optimization.
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available.
Cluster Analysis of a Symbolic Regression Search Space
In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space.
Complexity Measures for Multi-objective Symbolic Regression
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 a-priori.
Optimization Networks for Integrated Machine Learning
In this contribution we revisit the core principles of optimization networks and demonstrate their suitability for solving machine learning problems.
On the Effectiveness of Genetic Operations in Symbolic Regression
This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring.
Concept Drift Detection with Variable Interaction Networks
The current development of today's production industry towards seamless sensor-based monitoring is paving the way for concepts such as Predictive Maintenance.
Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression
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 symbolic regression: fast, online calculation of population diversity and algebraic simplification of symbolic expression trees.
Resource-constrained multi-project scheduling with activity and time flexibility
Project scheduling in manufacturing environments often requires flexibility in terms of the selection and the exact length of alternative production activities.
Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure
Diversity represents an important aspect of genetic programming, being directly correlated with search performance.
On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection
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 forecasting problems.
Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset
The approach is based on unguided symbolic regression (every variable present in the dataset is treated as the target variable in multiple regression runs) and a novel variable relevance metric for genetic programming.
Macro-Economic Time Series Modeling and Interaction Networks
In the proposed approach multiple symbolic regression runs are executed for each variable of the dataset to find potentially interesting models.
