A matlab version of relation model.
The code is collected in the RelationModel folder, supporting use on the latest PlatEMO-4.5 platform. Please place the RelationModel folder in the root directory of PlatEMO for easy access. You can also manually add the RelationModel folder to Matlab's search path.
- Three evolutionary algorithms based on the relation model have been implemented so far.
- In the RelationModel folder, the related functions are only initially implemented. The plan is to use the same code structure and function names as the Python version in future updates, to facilitate maintenance and updating. ❗️
- The implementation of other related algorithms is still in progress, so stay tuned. 🚗
We extend RCPS to the multi-objective problem (MOP), accelerating the convergence speed of multi-objective evolutionary algorithms by predicting approximations domination between two solutions through relation models. (code, paper)
Hao H, Zhou A, Zhang H. An approximated domination relationship based on binary classifiers for evolutionary multiobjective optimization[C]//2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021: 2427-2434.
Hao H, Zhou A, Qian H, et al. Expensive multiobjective optimization by relation learning and prediction[J]. IEEE Transactions on Evolutionary Computation, 2022, 26(5): 1157-1170.
In this work, by designing a relation model to assist the evolutionary algorithm in solving expensive multi-objective optimization problems, the paper proposes an adaptive classification strategy and a voting scoring strategy to enhance the performance of relation models. (code, paper)
Hao H, Zhou A. A Relation Surrogate Model for Expensive Multiobjective Continuous and Combinatorial Optimization[C]//International Conference on Evolutionary Multi-Criterion Optimization. Cham: Springer Nature Switzerland, 2023: 205-217.
We apply convolutional neural networks~(CNN) to the learning of relation data, effectively overcoming challenges related to data types and dimensions, and demonstrating good performance on continuous, discrete problems as well as mid-to-high dimensional issues. (code, paper)