Source Codes for Felix's Publications

Share Your Feedback (Thanks for sharing your comments and suggestions with us.)

Complex Networks
  • Performance Prediction:
    • newGraph Isomorphism Network-based Multitask robustness Analysis System >> GIN-MAS
      • A multitask learning approach is taken to learn the network robustness metrics, including connectivity robustness, controllability robustness, destruction threshold, and the maximum number of connected components.
      • A destruction-based robustness metric that is both practical and computationally efficient is formulated and used to measure network robustness in this work.
      • References:
        • A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network in IEEE Transactions on Cybernetics [Link]
    • newSpatial Pyramid Pooling Convolutional Neural Network >> SPP-CNN
      • A spatial pyramid pooling (SPP) is installed between the convolutional layers and fully-connected layer of CNN.
      • SPP-CNN has a wider tolerance to different input-data sizes, while maintaining fast approximation speed.
      • References:
        • SPP-CNN: An Efficient Framework for Network Robustness Prediction in IEEE Transactions on Circuits and Systems I: Regular Papers [Link]
    • Learning Feature Representation-based Convolutional Neural Network >> LFR-CNN
      • Higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction.
      • It is insensitive to input size, thus, the applicability is significantly extended.
      • References:
        • A Learning Convolutional Neural Network Approach for Network Robustness Prediction in IEEE Transactions on Cybernetics [Link]
    • Controllability Robustness Prediction >> PCR [Data]
      • A convolutional neural network (CNN) is used to predict the controllability robustness of complex networks under malicious attacks.
      • References:
        • Predicting Network Controllability Robustness: A Convolutional Neural Network Approach in IEEE Transactions on Cybernetics [Link]
    • Knowledge-based Controllability Robustness Prediction >> iPCR [Data]
      • An improved version of PCR.
      • Prior knowledge and multiple CNNs are used to predict the controllability robustness of complex networks.
      • References:
        • Knowledge-Based Prediction of Network Controllability Robustness in IEEE Transactions on Neural Networks and Learning Systems [Link]
  • Controllability Robustness:
    • q-snapback model >> QSN
      • References:
        • Toward Stronger Robustness of Network Controllability: A Snapback Network Model in IEEE Transactions on Circuits and Systems I: Regular Papers 2018
    • Controllability Robustness Comparison >> CRCMP
      • References:
        • A Comparative Study on Controllability Robustness of Complex Networks in IEEE Transactions on Circuits and Systems II: Express Briefs 2019
    • Empirical Necessary Condition >> ENC
      • References:
        • Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition in IEEE Transactions on Circuits and Systems I: Regular Papers 2020
    • Henneberg-growth Networks >> HG
      • References:
        • Controllability Robustness of Henneberg-growth Complex Networks in IEEE Access 2022
Evolutionary Computation
  • Evolutionary Benchmark Generator >> HFEBG
    • HFEBG (Hierarchical Fitness Evolutionary Benchmark Generator)
    • Both Kruskal-Wallis and Mann-Whitney tests are included
    • ver. 3.0
    • References:
      • On Constructing Alternative Benchmark Suite for Evolutionary Algorithms in Swarm and Evolutionary Computation 2019
      • Evolving Benchmark Functions for Optimization Algorithms in From Parallel to Emergent Computing 2019
      • Evolving Benchmark Functions Using Kruskal-Wallis Test in GECCO 2018
  • Non-revisiting Genetic Algorithm with Constant Memory >> cNrGACM
    • cNrGA CM (Non-revisiting Genetic Algorithm with Constant Memory)
    • References:
      • Non-revisiting Genetic Algorithm with Adaptive Mutation Using Constant Memory in Memetic Computing 2016
  • History-assisted Restart CMA-ES >> HRCMAES
    • HRCMAES (History-assisted Restart Covariance Matrix Adaptation Evolution Strategy)
    • ver. CEC 2019
    • References:
      • On-line Search History-assisted Restart Strategy for Covariance Matrix Adaptation Evolution Strategy in CEC 2019