Jul 2026· Proceedings of the Genetic and Evolutionary Computation Conference Companion· 0 citations· 14 references
TL;DR
An extension of the irace framework, h-irace, is proposed that incorporates concurrent racing processes over predefined instance subsets that supports configuration specialization across heterogeneous instance subsets in both continuous and combinatorial optimization problems.
Abstract
Automatic algorithm configuration is a well-established problem in optimization that becomes particularly challenging when dealing with heterogeneous sets of problem instances. In such scenarios, instance characteristics such as difficulty, structure, or dimensionality can induce substantial performance variability, leading to a trade-off between exploring diverse configurations for generalization and intensifying the search to improve performance. While specialized configurations for each instance type would be ideal, this is often unfeasible due to limited tuning budgets and the lack of reliable knowledge about instance similarity. To explore this issue, we propose an extension of the irace framework, h-irace, that incorporates concurrent racing processes over predefined instance subsets. The proposed approach is evaluated on two heterogeneous tuning scenarios using algorithms from the Ant Colony Optimization (ACO) and PSO-X families. The results show that the proposed method supports configuration specialization across heterogeneous instance subsets in both continuous and combinatorial optimization problems.
DyCA treats instance clustering as a co-evolving component within the search process, reusing accumulated evaluation data as feature-free signals to progressively partition instances with similar algorithmic response patterns, thereby enabling finer-grained and more adaptive guidance for specialized algorithm design.
Qinglong Hu, Qingfu Zhang, Fei Liu et al.· 0 citations
MODT-ASP, a multi-objective evolutionary framework designed to construct interpretable decision trees for ASP overcomes the limitations of existing methods, by employing a customized encoding scheme and specialized genetic operators to explore trade-offs between predictive accuracy and model complexity via Pareto optimization.
Matheus Guedes Vilas Boas, E. Wanner, Gladston J. P. Moreira· Annual Conference on Genetic...· 0 citations
It is found that none of the LLaMEA generated algorithms outperformed KMeans++, and prompting strategy affects the structural diversity of generated algorithms, with exemplar-injection producing the broadest range of algorithm families.
In many real-world applications, optimization problems do not appear in isolation but as streams of instances that need to be solved in sequence. Moreover, the features of the instances that compose a stream may change over time, influencing the performance of optimization algorithms. However, most work in automatic algorithm selection and configuration still focuses on static or offline settings, with only a few studies addressing the streaming case. Progress in this area may be hindered by the absence of a general, flexible benchmark that would allow researchers to evaluate new methods under controlled and reproducible settings. This paper presents a methodology for constructing benchmark streams of optimization problem instances, applicable to any domain equipped with a problem generator that allows generating intermediate instances between known classes. The approach enables fine-grained control over key stream characteristics, including the frequency and speed of transitions between different problem classes, and whether these changes affect instance features, algorithmic performance, or both. This flexibility allows the creation of diverse benchmarks, which can be used to stress-test existing and future algorithm selection and configuration approaches, revealing their performance under different stream types.
Margherita Battistotti, Manuel López-Ibáñez, Kate Smith-Miles et al.· Annual Conference on Genetic...· 0 citations
This work investigates how balancing the number of decomposed subproblems and ants impacts performance under a fixed computational budget and introduces MOEA/D-AM, a hybrid algorithm that adapts this decomposition strategy for partial classification rule induction.
Adel Abdelkader Mokadem, Marie-Éléonore Kessaci, Laetitia Jourdan et al.· Proceedings of the Genetic a...· 0 citations
Large Language Models (LLMs) are opening new directions for automated heuristic design (AHD), allowing evolutionary methods to create and enhance heuristics for constrained optimization problems (COPs). However, most existing approaches face the challenge of the exploration-exploitation balance, where the evolution needs to escape convergence to homogeneous populations and discover as large a heuristic landscape as possible. To address this challenge, we introduce Quality-Diversity Evolution (QDEvo), a multi-objective framework that integrates Quality-Diversity optimization with LLM-based AHD. At its core, QDEvo employs a semantic survival selection mechanism that clusters algorithms by functional similarity, then applies local Pareto competition. Evaluation on well-known COPs benchmarks and real-world problems shows that our method consistently outperforms the state-of-the-art baseline in both Hypervolume and Inverted Generational Distance metrics. These results facilitate further exploration of the algorithmic design space, while ensuring competitive solution quality and efficiency.
Nam Do Khanh, Nhat Nguyen Tran Minh, Dat Pham Vu Tuan et al.· Proceedings of the Genetic a...· 1 citation