no code implementations • 1 Apr 2024 • Haokai Hong, WanYu Lin, Kay Chen Tan
These structure variations are encoded with an equivariant encoder and treated as domain supervisors to control denoising.
no code implementations • 11 Dec 2023 • Haokai Hong, Min Jiang
However, existing multi-objective evolutionary algorithms (MOEAs) encounter significant challenges in generating high-quality populations when solving diverse complex MOPs.
no code implementations • 8 Apr 2023 • Haokai Hong, Min Jiang, Gary G. Yen
In this work, we propose an evolutionary algorithm for solving LSMOPs based on Monte Carlo tree search, the so-called LMMOCTS, which aims to improve the performance and insensitivity for large-scale multiobjective optimization problems.
no code implementations • 8 Apr 2023 • Haokai Hong, Min Jiang, Qiuzhen Lin, Kay Chen Tan
To sample the most suitable evolutionary directions for different solutions, Thompson sampling is adopted for its effectiveness in recommending from a very large number of items within limited historical evaluations.
no code implementations • 20 May 2022 • Haokai Hong, Min Jiang, Liang Feng, Qiuzhen Lin, Kay Chen Tan
However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm.
no code implementations • 16 Jul 2021 • Haokai Hong, Kai Ye, Min Jiang, Donglin Cao, Kay Chen Tan
At the same time, due to the adoption of an individual-based evolution mechanism, the computational cost of the proposed method is independent of the number of decision variables, thus avoiding the problem of exponential growth of the search space.
no code implementations • 8 Jan 2021 • Zhenzhong Wang, Haokai Hong, Kai Ye, Min Jiang, Kay Chen Tan
However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches.