A Group-based Approach to Improve Multifactorial Evolutionary Algorithm
Authors: Jing Tang, Yingke Chen, Zixuan Deng, Yanping Xiang, Colin Paul Joy
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct the experiments in both the cross-domain and intra-domain problems. |
| Researcher Affiliation | Academia | Jing Tang1, Yingke Chen1, Zixuan Deng2, Yanping Xiang2, Colin Paul Joy1 1 Department of Computer Science & Information Systems, Teesside University, UK 2 School of Computer Science & Engineering, University of Electronic Science and Technology of China, China |
| Pseudocode | Yes | Algorithm 1: Grouping Tasks |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing source code or a direct link to a code repository for the methodology described. |
| Open Datasets | Yes | We begin the cross-domain experiments with four benchmark functions for continuous optimisation that are commonly used in the closely related literature [Yew-Soon Ong et al., 2006; Chauhan et al., 2013; Nguyen et al., 2007; Le et al., 2009]: a) Griewank function... b) Ackley function... c) Rastrigin function... and d) Weierstrass function... |
| Dataset Splits | No | The paper defines the benchmark functions and their search spaces, but it does not specify explicit training/validation/test dataset splits as it involves continuous optimization over functions rather than static datasets with partitioned samples. |
| Hardware Specification | Yes | All experiments are performed on a desktop with Intel i7 CPU (3.4 GHz) and 8GB RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | D is set to 30, so the search dimension is 30. We present two sets of cross-domain experimental results... 50 tasks and a population of 250 individuals evolve over 200 generations. All the results are averaged over 20 independent runs... The random mating probability (rmp) of MFEA keeps the same parameter value 0.3 as in the original paper [Gupta et al., 2016]. GMFEA variants (GMF and GMF+S) have a full crossover and a mutation probability of 0.8 among members in the same group, and the normalised neighbour size value is set to 0.4. Due to the quasi-Newton method in MFEA variants that highly strengthens the local search, we resort to a relatively big α = 0.95 for a relatively small demand of a population diversity in the experiments. |