Search Results for author:
Found 4 papers, 0 papers with code
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.
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.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
