YNU Nakata Lab

Software

Surrogate-assisted multi-objective evolutionary algorithms

  1. SFA/MFDE

    • Language: Matlab
    • Relevant publication: Yuma Horaguchi and Masaya Nakata, High-Dimensional Expensive Multiobjective Optimization Using a Surrogate-Assisted Multifactorial Evolutionary Algorithm, The Genetic and Evolutionary Computation Conference (GECCO) 2025, accepted.

  2. DSEA/D

    • Language: Matlab
    • Relevant publication: Yuma Horaguchi and Masaya Nakata, A Dual Surrogate-based Evolutionary Algorithm for High-Dimensional Expensive Multiobjective Optimization Problems, Proceedings of IEEE Congress on Evolutionary Computation 2024, pp.01-08, July 2024. Paper

  3. SFA/DE

    • Language: Matlab
    • Relevant publication: Yuma Horaguchi, Kei Nishihara and Masaya Nakata, Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems, Swarm and Evolutionary Computation, Elsevier, April 2024, Vol. 86, 101516. Paper

  4. DR-MCEA/D

    • Language: Matlab
    • Relevant publication: Yuma Horaguchi and Masaya Nakata, High-Dimensional Expensive Optimization by Classification-based Multiobjective Evolutionary Algorithm with Dimensionality Reduction, Proceedings of SICE Annual Conference 2023, pp.1535-1542, SICE, September 2023. Paper

  5. MCEA/D

    • Language: Matlab
    • Relevant publication: Takumi Sonoda and Masaya Nakata, Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multiobjective Problems, IEEE Transactions on Evolutionary Computation, Vol.26, No.6, pp.1581-1595, IEEE, March 2022. Paper
Surrogate-assisted single-objective evolutionary algorithms

  1. SAPO

    • Language: Matlab
    • Relevant publication: Kei Nishihara and Masaya Nakata, A Surrogate-assisted Partial Optimization for Expensive Constrained Optimization Problems, Proceedings of 18th International Conference on Parallel Problem Solving from Nature (PPSN), pp.391–407, September 2024. Paper

  2. EBADE

    • Language: Python
    • Relevant publication: Kei Nishihara and Masaya Nakata, Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems, Vol. 10, pp.3633–3656, Complex & Intelligent Systems, Springer, 2024. Paper

  3. SADE-ATDSC

    • Language: Matlab
    • Relevant publication: Kei Nishihara and Masaya Nakata, Surrogate-assisted Differential Evolution with Adaptation of Training Data Selection Criterion, Proceedings of IEEE Symposium Series on Computational Intelligence 2022, pp.1675-1682, IEEE, Dec 2022. Paper
Evolutionary machine learning

  1. LCS with Adaptive Rule Representation

    • Language: Julia
    • Relevant publication: Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama, Keiki Takadama, Hisao Ishibuchi, and Masaya Nakata, Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems, IEEE Transactions on Evolutionary Computation, IEEE. Paper

  2. LFCS with Dempster-Shafer Theory

    • Language: Julia
    • Relevant publication: Hiroki Shiraishi, Hisao Ishibuchi, and Masaya Nakata, A Class Inference Scheme With Dempster-Shafer Theory for Learning Fuzzy-Classifier Systems, ACM Transactions on Evolutionary Learning and Optimization, ACM, February 2025. Paper

  3. XCS-theory

    • Language: C#
    • Relevant publication: Masaya Nakata and Will N. Browne, Learning Optimality Theory for Accuracy-based Learning Classifier System, IEEE Transactions on Evolutionary Computation, Vol.25, No.1, pp.61-74, IEEE, February 2021. Paper