Learning lessons from the common cold: How reducing parasite virulence improves coevolutionary optimization. - John Cartlidge; Seth Bullock (IEEE Congress on Evolutionary Computation, 2002)

Caring versus Sharing: How to Maintain Engagement and Diversity in Coevolving Populations. - John Cartlidge; Seth Bullock (ECAL, 2003)

Unpicking Tartan CIAO Plots: Understanding Irregular Coevolutionary Cycling. - John Cartlidge; Seth Bullock (Adapt. Behav., 2004)

Combating Coevolutionary Disengagement by Reducing Parasite Virulence. - John Cartlidge; Seth Bullock (Evol. Comput., 2004)

Rules of engagement : competitive coevolutionary dynamics in computational systems. - John Cartlidge (2004)

Dynamically adapting parasite virulence to combat coevolutionary disengagement. - John Cartlidge (ALIFE, 2008)

Modelling Resilience in Cloud-Scale Data Centres - John Cartlidge; Ilango Sriram (CoRR, 2011)

Autonomous Virulence Adaptation Improves Coevolutionary Optimization. - John Cartlidge; Djamel Ait-Boudaoud (IEEE Trans. Evol. Comput., 2011)

Too Fast Too Furious - Faster Financial-market Trading Agents Can Give Less Efficient Markets. - John Cartlidge; Charlotte Szostek; Marco De Luca; Dave Cliff (ICAART (2), 2012)

Flying by the seat of their pants: what can high frequency trading learn from aviation? - Gordon D. Baxter; John Cartlidge (ATACCS, 2013)

Comparison of Cloud Middleware Protocols and Subscription Network Topologies using CReST, the Cloud Research Simulation Toolkit - The Three Truths of Cloud Computing are: Hardware Fails, Software has Bugs, and People Make Mistakes. - John Cartlidge; Dave Cliff (CLOSER, 2013)

Evidencing the "Robot Phase Transition" in Human-agent Experimental Financial Markets. - John Cartlidge; Dave Cliff (ICAART (1), 2013)

Exploring Assignment-Adaptive (ASAD) Trading Agents in Financial Market Experiments. - Steve Stotter; John Cartlidge; Dave Cliff (ICAART (1), 2013)

Trading Experiments using Financial Agents in a Simulated Cloud Computing Commodity Market. - John Cartlidge (ICAART (2), 2014)

Correcting a financial brokerage model for cloud computing: closing the window of opportunity for commercialisation. - John Cartlidge; Philip Clamp (J. Cloud Comput., 2014)

Behavioural Investigations of Financial Trading Agents Using Exchange Portal (ExPo). - Steve Stotter; John Cartlidge; Dave Cliff (Trans. Comput. Collect. Intell., 2014)

Geographical Huff Model Calibration using Taxi Trajectory Data. - Shuhui Gong; John Cartlidge; Yang Yue 001; Guoping Qiu; Qingquan Li; Jingyu Xin (IWCTS@SIGSPATIAL, 2017)

Agent-Based Model Exploration of Latency Arbitrage in Fragmented Financial Markets. - Matthew Duffin; John Cartlidge (SSCI, 2018)

Optimisation of transportation service network using κ-node large neighbourhood search. - Ruibin Bai; John R. Woodward; Nachiappan Subramanian; John Cartlidge (Comput. Oper. Res., 2018)

MPC Joins the Dark Side. - John Cartlidge; Nigel P. Smart; Younes Talibi Alaoui (IACR Cryptol. ePrint Arch., 2018)

Fools Rush In: Competitive Effects of Reaction Time in Automated Trading. - Henry Hanifan; John Cartlidge (ICAART (1), 2020)

Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders. - Henry Hanifan; Ben Watson; John Cartlidge; Dave Cliff (ICAART (Revised Selected Papers), 2020)

Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation. - Shuhui Gong; John Cartlidge; Ruibin Bai; Yang Yue 001; Qingquan Li; Guoping Qiu (Int. J. Geogr. Inf. Sci., 2020)

The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural Network. - Zijian Shi; Yu Chen; John Cartlidge (AAAI, 2021)

Block Auction: A General Blockchain Protocol for Privacy-Preserving and Verifiable Periodic Double Auctions. - Theodoros Constantinides; John Cartlidge (Blockchain, 2021)

Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms. - Hugo Alcaraz-Herrera; John Cartlidge (IJCCI, 2021)

The Limit Order Book Recreation Model (LOBRM): An Extended Analysis. - Zijian Shi; John Cartlidge (ECML/PKDD (4), 2021)

Geographical and temporal huff model calibration using taxi trajectory data. - Shuhui Gong; John Cartlidge; Ruibin Bai; Yang Yue 001; Qingquan Li; Guoping Qiu (GeoInformatica, 2021)

Kicking-the-Bucket: Fast Privacy-Preserving Trading Using Buckets. - Mariana Botelho da Gama; John Cartlidge; Antigoni Polychroniadou; Nigel P. Smart; Younes Talibi Alaoui (IACR Cryptol. ePrint Arch., 2021)

Multi-party computation mechanism for anonymous equity block trading: A secure implementation of turquoise plato uncross. - John Cartlidge; Nigel P. Smart; Younes Talibi Alaoui (Intell. Syst. Account. Finance Manag., 2021)

Challenges Faced by Industries and Their Potential Solutions in Deploying Machine Learning Applications. - Raj Mani Shukla; John Cartlidge (CCWC, 2022)

Exploration of Ontological Representations for Evolutionary Computation. - Hugo Alcaraz-Herrera; John Cartlidge (CEC, 2022)

State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation. - Zijian Shi; John Cartlidge (KDD, 2022)

All for one and one for all: Fully decentralised privacy-preserving dark pool trading using multi-party computation. - Mariana Botelho da Gama; John Cartlidge; Nigel P. Smart; Younes Talibi Alaoui (IACR Cryptol. ePrint Arch., 2022)

EvoRecSys: Evolutionary framework for health and well-being recommender systems. - Hugo Alcaraz-Herrera; John Cartlidge; Zoi Toumpakari; Max Western; Iván Palomares (User Model. User Adapt. Interact., 2022)

Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology. - Zijian Shi; John Cartlidge (AAMAS, 2023)

Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks. - Lawrence Clegg; John Cartlidge (CoRR, 2023)

Using Coevolution and Substitution of the Fittest for Health and Well-Being Recommender Systems. - Hugo Alcaraz-Herrera; John Cartlidge (SN Comput. Sci., 2023)

DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction. - Zinuo You; Zijian Shi; Hongbo Bo; John Cartlidge; Li Zhang 131; Yan Ge (ICAART (2), 2024)

Multi-stock Movements Classification: A Decoupled Graph Diffusion Neural Network Approach. - Zinuo You; Zijian Shi; Hongbo Bo; John Cartlidge; Li Zhang 131; Yan Ge (ICAART (Revised Selected Paper, 2), 2024)

Multi-Relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends Classification. - Zinuo You; Pengju Zhang; Jin Zheng; John Cartlidge (ICASSP, 2024)

Simulation of Social Media-Driven Bubble Formation in Financial Markets using an Agent-Based Model with Hierarchical Influence Network. - Gonzalo Bohorquez; John Cartlidge (CoRR, 2024)

Artificial intelligence for collective intelligence: a national-scale research strategy. - Seth Bullock; Nirav Ajmeri; Mike Batty; Michaela Black; John Cartlidge; Robert Challen; Cangxiong Chen; Jing Chen; Joan Condell; Leon Danon; Adam Dennett; Alison J. Heppenstall; et al. (Knowl. Eng. Rev., 2024)

Neural stochastic agent-based limit order book simulation with neural point process and diffusion probabilistic model. - Zijian Shi; John Cartlidge (Intell. Syst. Account. Finance Manag., 2024)

Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations. - Yunhua Pei; Jin Zheng; John Cartlidge (ICAART (2), 2025)

zkMixer: A Configurable Zero-Knowledge Mixer with Proof of Innocence and Anti-Money Laundering Consensus Protocols. - Theodoros Constantinides; John Cartlidge (CoRR, 2025)

Cross-Modal Temporal Fusion for Financial Market Forecasting. - Yunhua Pei; John Cartlidge; Anandadeep Mandal; Daniel Gold; Enrique Marcilio; Riccardo Mazzon (CoRR, 2025)

Risk-aware black-box portfolio construction using Bayesian optimization with adaptive weighted Lagrangian estimator. - Zinuo You; John Cartlidge; Karen Elliott; Menghan Ge; Daniel Gold (CoRR, 2025)