8 citations · 28 across the 9 of their papers we have counts for
6 papers · 1 filter
Automated Metaheuristic Algorithm Design with Autoregressive Learning
Qi Zhao, Tengfei Liu, Bai Yan +3
Automated design of metaheuristic algorithms offers an attractive avenue to reduce human effort and gain enhanced performance beyond human intuition. Current automated methods desi…
Distributed Evolution Strategies with Multi-Level Learning for Large-Scale Black-Box Optimization
Qiqi Duan, Chang Shao, Guochen Zhou +3
In the post-Moore era, main performance gains of black-box optimizers are increasingly depending on parallelism, especially for large-scale optimization (LSO). Here we propose to p…
Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
Qiqi Duan, Chang Shao, Guochen Zhou +3
Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC)…
AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design
Qi Zhao, Bai Yan, Taiwei Hu +4
Metaheuristics are prominent gradient-free optimizers for solving hard problems that do not meet the rigorous mathematical assumptions of analytical solvers. The canonical manual o…
Automated Design of Metaheuristic Algorithms: A Survey
Qi Zhao, Qiqi Duan, Bai Yan +2
Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality…
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
Qiqi Duan, Guochen Zhou, Chang Shao +7
In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods (e.g., evolutionary algorithms, swarm intel…