Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
Authors: Chao Bian, Yawen Zhou, Miqing Li, Chao Qian
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analytically present that introducing randomness into the population update procedure in MOEAs can be beneficial for the search. More specifically, we prove that the expected running time of a well-established MOEA (SMS-EMOA) for solving a commonly studied biobjective problem, One Jump Zero Jump, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed stochastic population update method. |
| Researcher Affiliation | Academia | 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2School of Computer Science, University of Birmingham, Birmingham B15 2TT, U.K. |
| Pseudocode | Yes | Algorithm 1 SMS-EMOA, Algorithm 2 POPULATION UPDATE (Q), Algorithm 3 STOCHASTIC POPULATION UPDATE (Q) |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on the One Jump Zero Jump problem, which is a theoretical benchmark problem defined by mathematical functions, not a publicly available dataset with specific access information in the conventional sense. |
| Dataset Splits | No | The paper uses the One Jump Zero Jump problem, which is a theoretical benchmark. It describes experimental parameters like 'problem size n' and 'population size µ' but does not discuss training/validation/test dataset splits as it's not a typical machine learning setup with empirical data. |
| Hardware Specification | No | Section 5, |
| Software Dependencies | No | Section 5, |
| Experiment Setup | Yes | We set k to 2, the problem size n from 10 to 30, with a step of 5, and the population size µ=2(n 2k + 4), as suggested in Theorem 3. |