Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization
Authors: Shengjie Ren, Zhijia Qiu, Chao Bian, Miqing Li, Chao Qian
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This result is derived by rigorous running time analysis in both single-objective and multi-objective scenarios... Experiments are also conducted to validate the theoretical results. |
| Researcher Affiliation | Academia | Shengjie Ren1 , Zhijia Qiu1 , Chao Bian1 , Miqing Li2 and Chao Qian1 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China School of Artificial Intelligence, Nanjing University, Nanjing 210023, China 2School of Computer Science, University of Birmingham, Birmingham B15 2TT, U.K. |
| Pseudocode | Yes | Algorithm 1 NSGA-II |
| Open Source Code | No | No explicit statement or link providing access to the open-source code for the methodology described in this paper. |
| Open Datasets | No | The Jump problem as presented in Definition 1, is to maximize the number of 1-bits of a solution... The One Jump Zero Jump problem is a bi-objective counterpart of the Jump problem... The paper defines the problems but does not provide access information for a dataset. |
| Dataset Splits | No | The paper discusses theoretical analysis and simulations of evolutionary algorithms on defined mathematical problems (Jump, One Jump Zero Jump) rather than using empirical datasets with explicit train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only discusses the experimental setup for the algorithms. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, specific library versions) for replicating the experiments. |
| Experiment Setup | Yes | Specifically, we set the problem size n of Jump and One Jump Zero Jump from 10 to 30, with a step of 5, and set the parameter k of the two problems to 4. For (µ+1)-GA solving Jump, the population size µ is set to 2, as suggested in Theorem 1; while for NSGA-II and SMS-EMOA solving One Jump Zero Jump, the population size µ is set to 4(n 2k + 3) and 2(n 2k + 3), respectively, as suggested in Theorems 2 and 4. |