Quan Bai

Bing Xue

Sankalp Khanna

Chao Qian

Yi Mei

The five-volume proceedings set LNAI 16451-16455, constitutes the refereed proceedings of the 22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025, held in Wellington, New Zealand, in November 17–21, 2025. The 197 full papers and 89 short papers included in this book were carefully reviewed and selected from 679 submissions. The papers are organized in the following topical sections: Part I:Clustering, Representation Learning, and Learning Systems; Deep learning; Graph learning Part II: Evolutionary Learning, Transfer and Continual Learning; Real-world Applications Part III: Human-Centric AI; Large Language Models Part IV: Interpretability, Fairness, and Trustworthy AI; Multi-Agent; Natural Language Processing; Planning, Scheduling, and Optimisation Part V: Computer Vision; Time Series Analysis

22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025, Wellington, New Zealand, November 17–21, 2025, Proceedings, Part II

PRICAI 2025: Trends in Artificial Intelligence

Zhi-Hua Zhou

Yang Yu

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.

Evolutionary Learning: Advances in Theories and Algorithms

Chao Qian

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