Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Runtime Analysis of Somatic Contiguous Hypermutation Operators in MOEA/D Framework
Authors: Zhengxin Huang, Yuren Zhou2359-2366
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present a runtime analysis of using two CHM operators in MOEA/D framework for solving five benchmark MOPs, including four bi-objective and one many-objective problems. Our analyses show that the expected runtimes of CHM operators on the four bi-objective problems are better than or as good as that of the well-studied standard bit mutation operator. Moreover, using CHM operators in MOEA/D framework can improve the best known upper bound on the many-objective problem by a factor of n. This paper provides insight into understanding the optimization behavior of CHM operators in the well-known MOEA/D framework, and indicates that using the CHM operator in MOEA/D framework is a promising method for handling MOPs. |
| Researcher Affiliation | Academia | Zhengxin Huang,1,3 Yuren Zhou1,2, 1School of Data and Computer Scinece, Sun Yat-sen University, Guangzhou 510006, China 2Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou 510006, China 3Department of Computer Science and Information Technology, Youjiang Medical University for Nationalities, Baise 533000, China Email: EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 A simple decomposition-based MOEA Input: An MOP with m objectives, stop criterion, parameter H, the number of scalar optimization subproblems N, weight vectors {λ1, , λN} and neighbor size T. Output: A candidate Pareto optimal solution set P. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | In this paper, we present runtime analyses of a simple MOEA based on the MOEA/D framework with two typical CHM operators on optimizing five benchmark MOPs, which include four bi-objective and one many-objective problems. |
| Dataset Splits | No | As a theoretical runtime analysis paper, it does not involve empirical experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical runtime analysis and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | As in the previous runtime analyses on CHM1 and CHM2 operators (Jansen and Zarges 2011; 2014; Xia and Zhou 2018), we only consider the case of r = 1 in this analysis. ... Similar to runtime analysis in (Li et al. 2016), Tchebycheff approach is used in this paper. ... For Algorithm 1, we use the classical Das and Dennis s approach (1998) to generate the N evenly distributed weight vectors for all considered problems, i.e., taking λk [1..N] i [1..m] from { 0 H } such that m i=1 λk i = 1, where H is a positive integer and N = H+m 1 m 1 . |