Automated Metaheuristic Algorithm Design With Autoregressive Learning

Automated design of metaheuristic algorithms offers an attractive avenue to reduce human effort and gain enhanced performance beyond human intuition. Current automated methods design algorithms within a fixed structure and operate from scratch. This poses a clear gap toward fully discovering potenti...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 29; no. 5; pp. 2004 - 2018
Main Authors: Zhao, Qi, Liu, Tengfei, Yan, Bai, Duan, Qiqi, Yang, Jian, Shi, Yuhui
Format: Journal Article
Language:English
Published: IEEE 01.10.2025
Subjects:
Approximation algorithms
Automated design
evolutionary algorithm
learning
metaheuristic
Metaheuristics
Neurons
optimization
Problem-solving
Search problems
transformer
Transformers
Vectors
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Automated design of metaheuristic algorithms offers an attractive avenue to reduce human effort and gain enhanced performance beyond human intuition. Current automated methods design algorithms within a fixed structure and operate from scratch. This poses a clear gap toward fully discovering potentials over the metaheuristic family and fertilizing from prior design experience. To bridge the gap, this article proposes an autoregressive learning-based designer for automated design of metaheuristic algorithms. Our designer formulates metaheuristic algorithm design as a sequence generation task, and harnesses an autoregressive generative network to handle the task. This offers two advances. First, through autoregressive inference, the designer generates algorithms with diverse lengths and structures, enabling to fully discover potentials over the metaheuristic family. Second, prior design knowledge learned and accumulated in neurons of the designer can be retrieved for designing algorithms for future problems, paving the way to continual design of algorithms for open-ended problem solving. Extensive experiments on numeral benchmarks and real-world problems reveal that the proposed designer generates algorithms that outperform all human-created baselines on 24 out of 25 test problems. The generated algorithms display various structures and behaviors, reasonably fitting for different problem-solving contexts. Code is available at https://github.com/auto4opt/ALDes .
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2024.3464677