Qi Chen
Orcid: 0000-0001-9367-4757Affiliations:
- Victoria University of Wellington, Evolutionary Computation Research Group, New Zealand
According to our database1,
Qi Chen
authored at least 70 papers
between 2015 and 2024.
Collaborative distances:
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Bibliography
2024
A Semantic-Based Hoist Mutation Operator for Evolutionary Feature Construction in Regression.
IEEE Trans. Evol. Comput., December, 2024
IEEE Trans. Evol. Comput., December, 2024
IEEE Trans. Evol. Comput., October, 2024
Modular Multitree Genetic Programming for Evolutionary Feature Construction for Regression.
IEEE Trans. Evol. Comput., October, 2024
Multitree Genetic Programming With Feature-Based Transfer Learning for Symbolic Regression on Incomplete Data.
IEEE Trans. Cybern., July, 2024
Genetic Programming for Feature Selection Based on Feature Removal Impact in High-Dimensional Symbolic Regression.
IEEE Trans. Emerg. Top. Comput. Intell., June, 2024
A geometric semantic macro-crossover operator for evolutionary feature construction in regression.
Genet. Program. Evolvable Mach., June, 2024
CoRR, 2024
P-Mixup: Improving Generalization Performance of Evolutionary Feature Construction with Pessimistic Vicinal Risk Minimization.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVIII, 2024
A Semantic-based Hoist Mutation Operator for Evolutionary Feature Construction in Regression [Hot off the Press].
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024
Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for Regression.
Proceedings of the Genetic and Evolutionary Computation Conference, 2024
Multi-task Genetic Programming with Semantic based Crossover for Multi-output Regression.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024
Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points in Regression.
Proceedings of the Genetic Programming - 27th European Conference, 2024
A New Concordance Correlation Coefficient based Fitness Function for Genetic Programming for Symbolic Regression.
Proceedings of the IEEE Congress on Evolutionary Computation, 2024
Genetic Programming with Multi-Task Feature Selection for Alzheimer's Disease Diagnosis.
Proceedings of the IEEE Congress on Evolutionary Computation, 2024
Feature Selection for GPSR Based on Maximal Information Coefficient and Shapley Values.
Proceedings of the IEEE Congress on Evolutionary Computation, 2024
Proceedings of the AI 2024: Advances in Artificial Intelligence, 2024
2023
IEEE Trans. Pattern Anal. Mach. Intell., November, 2023
MAP-Elites for Genetic Programming-Based Ensemble Learning: An Interactive Approach [AI-eXplained].
IEEE Comput. Intell. Mag., November, 2023
IEEE Trans. Evol. Comput., 2023
Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction.
Proceedings of the PRICAI 2023: Trends in Artificial Intelligence, 2023
Genetic Programming-based Evolutionary Feature Construction for Heterogeneous Ensemble Learning [Hot of the Press].
Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023
A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for Regression.
Proceedings of the Genetic and Evolutionary Computation Conference, 2023
Fast and Efficient Local-Search for Genetic Programming Based Loss Function Learning.
Proceedings of the Genetic and Evolutionary Computation Conference, 2023
Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear Scaling.
Proceedings of the Genetic and Evolutionary Computation Conference, 2023
AMTEA-Based Multi-task Optimisation for Multi-objective Feature Selection in Classification.
Proceedings of the Applications of Evolutionary Computation - 26th European Conference, 2023
Proceedings of the Genetic Programming - 26th European Conference, 2023
Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression.
Proceedings of the Data Science and Machine Learning, 2023
Bloating Reduction in Symbolic Regression Through Function Frequency-Based Tree Substitution in Genetic Programming.
Proceedings of the AI 2023: Advances in Artificial Intelligence, 2023
2022
Rademacher Complexity for Enhancing the Generalization of Genetic Programming for Symbolic Regression.
IEEE Trans. Cybern., 2022
IEEE Trans. Cybern., 2022
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022
A New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment.
Proceedings of the Applications of Evolutionary Computation - 25th European Conference, 2022
Multi-objective Genetic Programming with the Adaptive Weighted Splines Representation for Symbolic Regression.
Proceedings of the Genetic Programming - 25th European Conference, 2022
2021
Preserving Population Diversity Based on Transformed Semantics in Genetic Programming for Symbolic Regression.
IEEE Trans. Evol. Comput., 2021
Multitree Genetic Programming With New Operators for Transfer Learning in Symbolic Regression With Incomplete Data.
IEEE Trans. Evol. Comput., 2021
A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data.
Soft Comput., 2021
Multi-objective genetic programming for symbolic regression with the adaptive weighted splines representation.
Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021
Proceedings of the IEEE Congress on Evolutionary Computation, 2021
Genetic Algorithm for Feature and Latent Variable Selection for Nutrient Assessment in Horticultural Products.
Proceedings of the IEEE Congress on Evolutionary Computation, 2021
GP with a Hybrid Tree-vector Representation for Instance Selection and Symbolic Regression on Incomplete Data.
Proceedings of the IEEE Congress on Evolutionary Computation, 2021
2020
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020
GP-based Feature Selection and Weighted KNN-based Instance Selection for Symbolic Regression with Incomplete Data.
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020
Adaptive weighted splines: a new representation to genetic programming for symbolic regression.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020
Multi-tree genetic programming for feature construction-based domain adaptation in symbolic regression with incomplete data.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020
Hessian Complexity Measure for Genetic Programming-Based Imputation Predictor Selection in Symbolic Regression with Incomplete Data.
Proceedings of the Genetic Programming - 23rd European Conference, 2020
Multi-Tree Genetic Programming-based Transformation for Transfer Learning in Symbolic Regression with Highly Incomplete Data.
Proceedings of the IEEE Congress on Evolutionary Computation, 2020
Genetic Programming with Noise Sensitivity for Imputation Predictor Selection in Symbolic Regression with Incomplete Data.
Proceedings of the IEEE Congress on Evolutionary Computation, 2020
Genetic Programming-Based Selection of Imputation Methods in Symbolic Regression with Missing Values.
Proceedings of the AI 2020: Advances in Artificial Intelligence, 2020
2019
Structural Risk Minimization-Driven Genetic Programming for Enhancing Generalization in Symbolic Regression.
IEEE Trans. Evol. Comput., 2019
Improving Generalization of Genetic Programming for Symbolic Regression With Angle-Driven Geometric Semantic Operators.
IEEE Trans. Evol. Comput., 2019
A Genetic Programming-based Wrapper Imputation Method for Symbolic Regression with Incomplete Data.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2019
Differential evolution for instance based transfer learning in genetic programming for symbolic regression.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019
Proceedings of the IEEE Congress on Evolutionary Computation, 2019
Proceedings of the IEEE Congress on Evolutionary Computation, 2019
Genetic Programming for Imputation Predictor Selection and Ranking in Symbolic Regression with High-Dimensional Incomplete Data.
Proceedings of the AI 2019: Advances in Artificial Intelligence, 2019
Genetic Programming-Based Simultaneous Feature Selection and Imputation for Symbolic Regression with Incomplete Data.
Proceedings of the Pattern Recognition - 5th Asian Conference, 2019
2018
PhD thesis, 2018
Proceedings of the AI 2018: Advances in Artificial Intelligence, 2018
2017
Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression.
IEEE Trans. Evol. Comput., 2017
Geometric Semantic Genetic Programming with Perpendicular Crossover and Random Segment Mutation for Symbolic Regression.
Proceedings of the Simulated Evolution and Learning - 11th International Conference, 2017
New geometric semantic operators in genetic programming: perpendicular crossover and random segment mutation.
Proceedings of the Genetic and Evolutionary Computation Conference, 2017
Geometric Semantic Crossover with an Angle-Aware Mating Scheme in Genetic Programming for Symbolic Regression.
Proceedings of the Genetic Programming - 20th European Conference, 2017
2016
Proceedings of the Intelligent and Evolutionary Systems - The 20th Asia Pacific Symposium, 2016
Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation.
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016
Improving generalisation of genetic programming for high-dimensional symbolic regression with feature selection.
Proceedings of the IEEE Congress on Evolutionary Computation, 2016
2015
Generalisation and domain adaptation in GP with gradient descent for symbolic regression.
Proceedings of the IEEE Congress on Evolutionary Computation, 2015