Lecture
Joint Lectures on Evolutionary Algorithms - February 2025
- Sofoklis Kitharidis (Leiden University) and Maria Laura Santoni (Sorbonne Université)
- Date
- Wednesday 5 February 2025
- Time
- Address
- Online
Talk 1: TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification - Sofoklis Kitharidis
In time-series classification, understanding model decisionsis crucial for their application in high-stakes domains such ashealthcare and finance. Counterfactual explanations, which provide insights by presenting alternative inputs that change model predictions, offer a promising solution. However, existing methods for generating counterfactual explanations for time-series data often struggle with balancing key objectives like proximity, sparsity, and validity. TX-Gen is a novel algorithm for generating counterfactual explanations based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). TX-Gen leverages evolutionary multi-objective optimization to find a diverse set of counterfactuals that are both sparse and valid, while maintaining minimal dissimilarity to the original time series. By incorporating a flexible reference-guided mechanism, our method improves the plausibility and interpretability of the counterfactualswithout relying on predefined assumptions. Extensive experiments on benchmark datasets demonstrate that TX-Gen outperforms existing methods in generating high-quality counterfactuals, making time-series models more transparent and interpretable.
Talk 2: Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization - Maria Laura Santoni
Rather than obtaining a single good solution for a given optimization problem, users often seek alternative design choices, because the best-found solution may perform poorly with respect to additional objectives or constraints that are difficult to capture into the modeling process. Aiming for batches of diverse solutions of high quality is often desirable, as it provides flexibility to accommodate post-hoc user preferences. At the same time, it is crucial that the quality of the best solution found is not compromised. One particular problem setting balancing high quality and diversity is fixing the required minimum distance between solutions while simultaneously obtaining the best possible fitness. We first show that this setting is not well addressed by state-of-the-art algorithms, performing in par or worse than pure random sampling. Driven by this important limitation, we propose a new approach, where parallel runs of the covariance matrix adaptation evolution strategy (CMA-ES) inherit tabu regions in a cascading fashion. We empirically demonstrate that our CMA-ES-Diversity Search (CMA-ES-DS) algorithm generates trajectories that allow to extract high-quality solution batches that respect a given minimum distance requirement, clearly outperforming those obtained from off-the-shelf random sampling, multi-modal optimization algorithms, and standard CMA-ES.
This series is organized by a team from four universities, initiated by prof. dr. Thomas Bäck (Leiden University), Assistant professor dr. Anna Kononova (Leiden University), prof. dr. Peter A.N. Bosman (CWI), prof. dr. Gusz Eiben (Vrije Universiteit Amsterdam), and dr. ir. Dirk Thierens (Utrecht University).