Publications
My Google Scholar profile shows 12,000+ citations and an h-index of 56.
Selected Publications
A selection of my most cited work, grouped by research area.
Large-Scale Optimisation
Decomposing very large problems into smaller, interacting sub-problems so that they can be solved cooperatively.
- Cooperative Co-evolution with Differential Grouping for Large Scale Optimization IEEE Transactions on Evolutionary Computation, 18(3):378–393, 2014 TEVC Outstanding Paper Award
Introduces differential grouping, which detects interactions between decision variables automatically and uses them to decompose a large-scale problem for cooperative co-evolution. It is widely used as a decomposition method in large-scale black-box optimisation.
- A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization ACM Transactions on Mathematical Software, 42(2):13:1–24, 2016
Combines automatic variable grouping with a powerful sub-problem optimiser in a divide-and-conquer framework, released with an open-source MATLAB implementation.
- Cooperative Coevolution with Route Distance Grouping for Large-Scale Capacitated Arc Routing Problems IEEE Transactions on Evolutionary Computation, 18(3):435–449, 2014
Brings cooperative co-evolution to large-scale arc routing: route distance grouping uses the distances between routes to decide which parts of the solution should be optimised together.
Genetic Programming for Scheduling
Automatically learning scheduling heuristics for dynamic, uncertain production environments with genetic programming (GP).
- Genetic Programming for Production Scheduling: A Survey with a Unified Framework Complex & Intelligent Systems, 3(1):41–66, 2017
A survey that organises GP-based hyper-heuristics for production scheduling into a unified framework, covering representations, evaluation, and directions for future research.
- Evolving Scheduling Heuristics via Genetic Programming with Feature Selection in Dynamic Flexible Job-Shop Scheduling IEEE Transactions on Cybernetics, 51(4):1797–1811, 2021
Integrates feature selection into GP so that the evolved routing and sequencing rules use only the most relevant features, giving smaller and more interpretable heuristics without losing effectiveness.
- Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling IEEE Transactions on Evolutionary Computation, 25(4):651–665, 2021
Learns scheduling heuristics for several related scheduling tasks at once, sharing knowledge between tasks and using surrogate models to cut the computational cost of evaluation.
- Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling IEEE Transactions on Evolutionary Computation, 28(1):147–167, 2024
A comprehensive review of how GP and other machine learning techniques are used to design heuristics for job shop scheduling, with a discussion of open challenges.
Routing and Logistics
Memetic and swarm-based algorithms for arc routing and electric vehicle routing.
- A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem IEEE Transactions on Cybernetics, 52(10):10855–10868, 2022
Tackles electric vehicle routing by separating the routing decisions from the charging decisions into two levels, solved with ant colony optimisation.
- Decomposition-Based Memetic Algorithm for Multiobjective Capacitated Arc Routing Problem IEEE Transactions on Evolutionary Computation, 15(2):151–165, 2011
Extends memetic search to multi-objective arc routing through decomposition, finding good trade-offs between total cost and makespan.
- Memetic Algorithm with Extended Neighborhood Search for Capacitated Arc Routing Problems IEEE Transactions on Evolutionary Computation, 13(5):1151–1166, 2009
Proposes MAENS, a memetic algorithm whose large-step Merge-Split operator explores far beyond traditional local moves. It became a widely used benchmark algorithm for arc routing.
Surveys on Evolutionary Machine Learning and Explainable AI
Overviews that map out emerging research areas.
- A Survey on Evolutionary Machine Learning Journal of the Royal Society of New Zealand, 49(2):205–228, 2019
Reviews how evolutionary computation is used across machine learning, including classification, feature selection, clustering, neural architecture search and computer vision.
- Explainable Artificial Intelligence by Genetic Programming: A Survey IEEE Transactions on Evolutionary Computation, 27(3):621–641, 2023
Surveys how GP contributes to explainable AI, both by evolving models that are interpretable by design and by producing explanations for black-box models.
Full Publication Lists
If you need the PDF of any of the papers, please feel free to contact me.