Runtime Analysis of Somatic Contiguous Hypermutation Operators in MOEA/D Framework
Authors: Zhengxin Huang, Yuren Zhou2359-2366
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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: thenewyi@gmail.com, zhouyuren@mail.sysu.edu.cn |
| 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 . |