Landscape features in single-objective continuous optimization: Have we hit a wall in algorithm selection generalization?

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-02-28 DOI:10.1016/j.swevo.2025.101894

Gjorgjina Cenikj , Gašper Petelin , Moritz Seiler , Nikola Cenikj , Tome Eftimov

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引用次数: 0

Abstract

The process of identifying the most suitable optimization algorithm for a specific problem, referred to as algorithm selection (AS), entails training models that leverage problem landscape features to forecast algorithm performance. A significant challenge in this domain is ensuring that AS models can generalize effectively to novel, unseen problems. This study evaluates the generalizability of AS models based on different problem representations in the context of single-objective continuous optimization. In particular, it considers the most widely used Exploratory Landscape Analysis features, as well as recently proposed Topological Landscape Analysis features, and features based on deep learning, such as DeepELA, TransOptAS and Doe2Vec. Our results indicate that when presented with out-of-distribution evaluation data, none of the feature-based AS models outperform a simple baseline model, i.e., a Single Best Solver.

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单目标连续优化中的景观特征：我们在算法选择泛化方面是否遇到了瓶颈？

为特定问题确定最合适的优化算法的过程，称为算法选择（as），需要训练模型，该模型利用问题概况特征来预测算法性能。这个领域的一个重大挑战是确保AS模型能够有效地推广到新的、看不见的问题。本研究在单目标连续优化的背景下，评估了基于不同问题表示的AS模型的泛化性。特别是，它考虑了最广泛使用的探索性景观分析特征，以及最近提出的拓扑景观分析特征，以及基于深度学习的特征，如DeepELA， TransOptAS和Doe2Vec。我们的研究结果表明，当提供分布外评估数据时，没有一个基于特征的AS模型优于简单的基线模型，即单个最佳求解器。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.