Surrogate-assisted evolutionary algorithms have been intensively used to solve computationally expensive problems with some success. However, traditional evolutionary algorithms are not suitable to deal with high-dimensional expensive problems (HEPs) with high-dimensional search space even if their fitness evaluations are assisted by surrogate models. The recently proposed autoencoder-embedded evolutionary optimization (AEO) framework is highly appropriate to deal with high-dimensional problems. This work aims to incorporate surrogate models into it to further boost its performance, thus resulting in surrogate-assisted autoencoder-embedded evolutionary optimization (SAEO). It proposes a novel model management strategy that can guarantee reasonable amounts of re-evaluations, hence the accuracy of surrogate models can be enhanced via being updated with new evaluated samples. Moreover, to ensure enough data samples before constructing surrogates, a problem-dimensionality-dependent activation condition is developed for incorporating surrogates into the SAEO framework. SAEO is tested on seven commonly used benchmark functions and compared with state-of-the-art algorithms for HEPs. The experimental results show that SAEO can further enhance the performance of AEO on most cases and SAEO performs significantly better than other algorithms. Therefore, SAEO has great potential to deal with HEPs.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computational Theory and Mathematics
- Computational modeling
- Data models
- High-dimensional optimization
- Prediction algorithms
- Predictive models
- expensive problems.
- surrogate models