Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis
Authors: Rung-Tzuo Liaw, Chuan-Kang Ting4295-4303
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This study examines the effectiveness and efficiency of the SBO on a suite of many-tasking benchmark problems, designed to deal with 30 tasks simultaneously. The experimental results show that SBO leads to better solutions and faster convergence than the state-of-the-art evolutionary multitasking algorithms. |
| Researcher Affiliation | Academia | Rung-Tzuo Liaw, Chuan-Kang Ting Department of Power Mechanical Engineering National Tsing Hua University Hsinchu 30013, Taiwan rtliaw@mx.nthu.edu.tw, ckting@pme.nthu.edu.tw |
| Pseudocode | Yes | Algorithm 1 Symbiosis in biocoenosis optimization; Algorithm 2 Update of symbiosis; Algorithm 3 Update of transfer rates. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | Yes | This study presents a test suite based on the benchmark functions of CEC 2017 competition (Awad et al. 2016; Liaw and Ting 2017). |
| Dataset Splits | No | The paper uses benchmark problems and conducts 30 trials, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts for the benchmark functions themselves, as is common for typical machine learning datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory, or cluster specifications). |
| Software Dependencies | No | The paper mentions using GA and CMAES, and refers to MFEA and EBS, but it does not specify any version numbers for these algorithms, libraries, or other software components. |
| Experiment Setup | Yes | Table 2 lists the parameter setting used in the following experiments. The beneficial and harmful factors are set to 0.25 and 0.50, respectively. The setting of MFEA follows the use of simulated binary crossover, polynomial mutation, and rmp set to 0.3 in (Gupta, Ong, and Feng 2016). The population size is set to 50m to better handling the many-tasking benchmarks. All experiments run over 30 trials due to the stochastic nature of EAs. |