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School of Electronic Engineering and Computer Science

Dr John Woodward

John

Senior Lecturer

Email: j.woodward@qmul.ac.uk
Telephone: +44 20 7882 5813
Room Number: Engineering, Eng 212

Teaching

Introductory Java Programming (BUPT joint programme)

 

Research

Publications

    • Wang P, Jin N, Woo WL et al. (2022), Noise tolerant drift detection method for data stream mining $nameOfConference


    • Tauritz DR, Woodward J (2022), Generative hyper-heuristics $nameOfConference


    • Haraldsson S, Brownlee A, Woodward JR et al. (2022), Genetic improvement: Taking real-world source code and improving it using computational search methods $nameOfConference


    • Qiao J, Woodward JR, Alam AS (2022), Educational Video Games for Learning English Vocabulary $nameOfConference


    • Craven MJ, Woodward JR (2022), Evolution of group-theoretic cryptology attacks using hyper-heuristics $nameOfConference


    • Winter E, Bowes D, Counsell S et al. (2022), How Do Developers <italic>Really</italic> Feel About Bug Fixing? Directions for Automatic Program Repair $nameOfConference


    • Winter ER, Nowack V, Bowes D et al. (2022), Let's Talk With Developers, Not About Developers: A Review of Automatic Program Repair Research $nameOfConference


    • Hong L, Woodward JR, Özcan E et al. (2021), Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming $nameOfConference


    • Gan Y, Chen X, Xie J et al. (2021), Natural SQL: Making SQL Easier to Infer from Natural Language Specifications Findings of the Association for Computational Linguistics: EMNLP 2021


    • Wang X, Brownlee AEI, Weiszer M et al. (2021), A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties $nameOfConference


    • Gan Y, Chen X, Xie J et al. (2021), Natural SQL: Making SQL Easier to Infer from Natural Language Specifications $nameOfConference


    • Haraldsson S, Brownlee A, Woodward JR et al. (2021), Genetic improvement: Taking real-world source code and improving it using genetic programming $nameOfConference


    • Tauritz DR, Woodward J (2021), Hyper-heuristics tutorial $nameOfConference


    • Krari ME, Guibadj RN, Woodward J et al. (2021), Introducing a hash function for the travelling salesman problem for differentiating solutions $nameOfConference


    • Kirbas S, Windels E, Mcbello O et al. (2021), On the Introduction of Automatic Program Repair in Bloomberg $nameOfConference


    • Wang X, Gu Y, Wu G et al. (2021), Robust scheduling for multiple agile Earth observation satellites under cloud coverage uncertainty $nameOfConference


    • Rezaei MJ, Woodward JR, Ramírez J et al. (2021), A novel two-stage heart arrhythmia ensemble classifier $nameOfConference


    • De Lemos F, Woodward J (2021), Calculating block time and consumed fuel for an aircraft model $nameOfConference


    • Rezaei MJ, Woodward JR, Ramirez J et al. (2021), Combination of Isolation Forest, SMOTE and Ensemble Learning for the classification of Atrial Fibrillation and Ventricular Arrhythmia $nameOfConference


    • Nowack V, Bowes D, Counsell S et al. (2021), Expanding Fix Patterns to Enable Automatic Program Repair $nameOfConference


    • Gan Y, Chen X, Xie J et al. (2021), Natural SQL: Making SQL Easier to Infer from Natural Language Specifications $nameOfConference

    • Gan Y, Chen X, Huang Q et al. (2021), Towards robustness of text-to-SQL models against synonym substitution $nameOfConference


    • Wang X, Brownlee AEI, Woodward JR et al. (2020), Aircraft taxi time prediction: Feature importance and their implications $nameOfConference


    • Rezaei MJ, Woodward JR, Ramirez J et al. (2020), Data Augmentation for Heart Arrhythmia Classification $nameOfConference


    • Volkovas R, Fairbank M, Woodward JR et al. (2020), Practical Game Design Tool: State Explorer $nameOfConference


    • Nowack V, Bowes D, Counsell S et al. (2020), Exploiting Fault Localisation for Efficient Program Repair GECCO ’20 Companion

    • Haraldsson S, Woodward JR, Wagner M (2020), Genetic improvement: Taking real-world source code and improving it using genetic programming $nameOfConference


    • Tauritz DR, Woodward J (2020), Hyper-heuristics tutorial $nameOfConference


    • Winter E, Bowes D, Counsell S et al. (2020), Human Factors in the Study of Automatic Software Repair: Future Directions for Research with Industry $nameOfConference


    • Martin SP, Craven MJ, Woodward JR (2020), A structured approach to modifying successful heuristics $nameOfConference


    • Wang S, Drake JH, Fairbrother J et al. (2019), A Constructive Heuristic Approach for Single Airport Slot Allocation Problems $nameOfConference


    • Volkovas R, Fairbank M, Woodward JR et al. (2019), Extracting learning curves from puzzle games $nameOfConference


    • Volkovas R, Fairbank M, Woodward JR et al. (2019), Mek: Mechanics prototyping tool for 2d tile-based turn-based deterministic games $nameOfConference


    • Warriar VR, Woodward JR, Tokarchuk L (2019), Modelling player preferences in AR mobile games IEEE Conference on Computatonal Intelligence and Game


    • Warriar VR, Ugarte C, Woodward JR et al. (2019), PlayMapper: Illuminating design spaces of platform games IEEE Conference on Computatonal Intelligence and Game


    • Tauritz DR, Woodward J (2019), Hyper-heuristics Tutorial $nameOfConference


    • Volkovas R, Fairbank M, Woodward J et al. (2019), Mek: Mechanics Prototyping Tool for 2D Tile-Based Turn-Based Deterministic Games $nameOfConference


    • Lucas S, Liu J, Bravi I et al. (2019), Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best Game Simulations Workshop (AAAI)

    • Burke EK, Hyde MR, Kendall G et al. (2019), A classification of hyper-heuristic approaches: Revisited $nameOfConference


    • Pappa GL, Emmerich MTM, Bazzan A et al. (2018), Tutorials at PPSN 2018 $nameOfConference


    • Tauritz DR, Woodward J (2018), Hyper-heuristics tutorial $nameOfConference


    • Christie LA, Brownlee AEI, Woodward JR (2018), Investigating benchmark correlations when comparing algorithms with parameter tuning $nameOfConference


    • Brownlee AEI, Woodward JR, Veerapen N (2018), Relating training instances to automatic design of algorithms for bin packing via features $nameOfConference


    • Brownlee AEI, Weiszer M, Woodward JR et al. (2018), A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing $nameOfConference


    • Petke J, Haraldsson SO, Harman M et al. (2018), Genetic Improvement of Software: A Comprehensive Survey $nameOfConference


    • Brownlee AEI, WEISZER M, CHEN J et al. (2018), A Fuzzy Approach to Addressing Uncertainty in Airport Ground Movement Optimisation $nameOfConference


    • DRAKE J, woodward JR (2017), A Hyper-heuristic Approach to Automated Generation of Mutation Operators for Evolutionary Programming $nameOfConference


    • WOODWARD JR (2017), The use of predictive models in dynamic treatment planning $nameOfConference


    • Haraldsson SO, Woodward JR, Brownlee AEI et al. (2017), Fixing bugs in your sleep: How genetic improvement became an overnight success $nameOfConference


    • Haraldsson SO, Woodward JR, Brownlee AEI et al. (2017), Genetic Improvement of Runtime and its Fitness Landscape in a Bioinformatics Application $nameOfConference


    • Tauritz DR, Woodward J (2017), Hyper-heuristics tutorial $nameOfConference


    • Haraldsson SO, Woodward JR, Brownlee AIE (2017), The use of automatic test data generation for genetic improvement in a live system $nameOfConference


    • Bai R, Woodward JR, Subramanian N et al. (2017), Optimisation of transportation service network using κ -node large neighbourhood search $nameOfConference


    • Bai R, Woodward JR, Subramanian N (2017), A new fast large neighbourhood search for service network design with asset balance constraints $nameOfConference


    • Haraldsson SO, Woodward JR, Brownlee AEI et al. (2017), Exploring fitness and edit distance of mutated python programs $nameOfConference


    • Benlic U, Burke EK, Woodward JR (2017), Breakout local search for the multi-objective gate allocation problem $nameOfConference


    • Hong L, Drake JH, Woodward JR et al. (2016), Automatically designing more general mutation operators of Evolutionary Programming for groups of function classes using a hyper-heuristic $nameOfConference


    • Woodward JR, Johnson CG, Brownlee AEI (2016), Connecting automatic parameter tuning, genetic programming as a hyper-heuristic, and genetic improvement programming $nameOfConference


    • López-Ibáez M, Tauritz D, Woodward J (2016), ECADA 2016 chairs' welcome $nameOfConference


    • Woodward JR, Brownlee AEI, Johnson CG (2016), Evals is not enough: Why we should report wall-clock time $nameOfConference


    • Woodward JR, Johnson CG, Brownlee AEI (2016), GP vs GI: If you can't beat them, join them $nameOfConference


    • Tauritz DR, Woodward J (2016), Hyper-heuristics $nameOfConference


    • Johnson CG, Woodward JR (2015), Fitness as task-relevant information accumulation $nameOfConference


    • Haraldsson SO, Woodward JR (2015), Genetic improvement of energy usage is only as reliable as the measurements are accurate $nameOfConference


    • Woodward J, Tauritz DR (2015), Hyper-heuristics tutorial $nameOfConference


    • Brownlee AEI, Woodward JR, Swan J (2015), Metaheuristic design pattern: Surrogate fitness functions $nameOfConference


    • Woodward J, Tauritz D, López-Ibáñez M (2015), Session details: ECADA'15 Workshop Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation


    • Pappa GL, Ochoa G, Hyde MR et al. (2014), Contrasting meta-learning and hyper-heuristic research: The role of evolutionary algorithms $nameOfConference


    • Swan J, Woodward J, Özcan E et al. (2014), Searching the Hyper-heuristic Design Space $nameOfConference


    • Brownlee AEI, Atkin JAD, Woodward JR et al. (2014), Airport ground movement: Real world data sets and approaches to handling uncertainty $nameOfConference

    • Haraldsson SO, Woodward JR (2014), Automated design of algorithms and genetic improvement: Contrast and commonalities $nameOfConference


    • Woodward J, Martin S, Swan J (2014), Benchmarks that matter for genetic programming $nameOfConference


    • Chuang Y, Chen L, Chen G et al. (2014), Isophote based center-surround contrast computation for image saliency detection $nameOfConference


    • Woodward J, Swan J (2014), Template method hyper-heuristics $nameOfConference


    • Woodward J, Swan J, Martin S (2014), The 'composite' design pattern in metaheuristics $nameOfConference


    • Pappa GL, Woodward J, Swan J et al. (2013), Session details: 3rd workshop on evolutionary computation for the automated design of algorithms Proceedings of the 15th annual conference companion on Genetic and evolutionary computation


    • Majid A, Chen L, Chen G et al. (2013), A context-aware personalized travel recommendation system based on geotagged social media data mining $nameOfConference


    • Swan J, Drake J, Özcan E et al. (2013), A comparison of acceptance criteria for the daily car-pooling problem $nameOfConference


    • Woodward J, Swan J (2013), A syntactic approach to prediction $nameOfConference


    • Cui T, Li J, Woodward JR et al. (2013), An ensemble based Genetic Programming system to predict English football premier league games $nameOfConference


    • Hong L, Woodward J, Li J et al. (2013), Automated design of probability distributions as mutation operators for evolutionary programming using genetic programming $nameOfConference


    • Burke EK, Hyde MR, Kendall G et al. (2012), Automating the packing heuristic design process with genetic programming $nameOfConference


    • Woodward J, Swan J (2012), The automatic generation of mutation operators for Genetic Algorithms $nameOfConference


    • Chen L, Lv M, Ye Q et al. (2011), A personal route prediction system based on trajectory data mining $nameOfConference


    • Woodward JR, Swan J (2011), Automatically designing selection heuristics $nameOfConference


    • Woodward JR (2010), The Necessity of meta bias in search algorithms $nameOfConference


    • Woodward JR, Swan J (2010), Why classifying search algorithms is essential $nameOfConference


    • Woodward JR, Gindy N (2010), A hyper-heuristic multi-criteria decision support system for eco-efficient product life cycle $nameOfConference


    • Woodward J (2010), Gisele L. Pappa, Alex Freitas: Automating the design of data mining algorithms, an evolutionary computation approach $nameOfConference


    • Burke EK, Hyde M, Kendall G et al. (2010), A Classification of Hyper-heuristic Approaches $nameOfConference


    • Burke EK, Hyde M, Kendall G et al. (2010), A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics $nameOfConference


    • Woodward JR, Farjudian A (2010), Artificial life, the second law of thermodynamics, and Kolmogorov complexity $nameOfConference


    • Woodward JR (2009), Computable and incomputable functions and search algorithms $nameOfConference


    • Woodward JR, Bai R (2009), Canonical representation genetic programming $nameOfConference


    • Burke EK, Hyde MR, Kendall G et al. (2009), Exploring hyper-heuristic methodologies with genetic programming $nameOfConference


    • Woodward JR, Bai R (2009), Why evolution is not a good paradigm for program induction; A critique of genetic programming $nameOfConference


    • Poli R, Woodward J, Burke EK (2007), A histogram-matching approach to the evolution of bin-packing strategies $nameOfConference


    • Burke EK, Hyde MR, Kendall G et al. (2007), The scalability of evolved on line bin packing heuristics $nameOfConference


    • Burke EK, Hyde MR, Kendall G et al. (2007), Automatic heuristic generation with genetic programming: Evolving a jack-of-all-trades or a master of one $nameOfConference


    • Woodward JR (2006), Complexity and Cartesian genetic programming $nameOfConference


    • Woodward JR (2006), Invariance of function complexity under primitive recursive functions $nameOfConference


    • Woodward J (2005), Complexity and cartesian genetic programming $nameOfConference

    • Woodward J (2005), Invariaiice of function complexity under primitive reeursive functions $nameOfConference

    • Woodward JR (2003), Evolving turing complete representations $nameOfConference


    • Woodward JR (2003), GA or GP? That is not the question $nameOfConference


    • Woodward JR (2003), Modularity in Genetic Programming $nameOfConference


    • Woodward JR, Neil JR (2003), No free lunch, program induction and combinatorial problems $nameOfConference


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