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Found 7 papers, 1 papers with code
A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories
To guide the design of better iterative optimisation heuristics, it is imperative to understand how inherent structural biases within algorithm components affect the performance on a wide variety of search landscapes.
A Functional Analysis Approach to Symbolic Regression
The superior performance of the proposed algorithm and insights into the limitations of GP open the way for further advancing GP for SR and related areas of explainable machine learning.
Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification.
Explainable Benchmarking for Iterative Optimization Heuristics
Introducing the IOH-Xplainer software framework, for analyzing and understanding the performance of various optimization algorithms and the impact of their different components and hyper-parameters.
Clustering-based Domain-Incremental Learning
A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a "new task".
Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems
Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics.
Challenges of ELA-guided Function Evolution using Genetic Programming
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years.
