School of Computing

Publications by Dr Michael Kampouridis

Also view these in the Kent Academic Repository

Article
Alexandridis, A., Kampouridis, M. and Cramer, S. (2017). A Comparison between Wavelet Networks and Genetic Programming in the Context of Temperature Derivatives. International Journal of Forecasting [Online] 33:21-47. Available at: http://www.sciencedirect.com/science/article/pii/S0169207016300711.
Cramer, S. et al. (2017). An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Systems with Applications [Online] 85:169-181. Available at: https://doi.org/10.1016/j.eswa.2017.05.029.
Kampouridis, M. and Otero, F. (2017). Heuristic procedures for improving the predictability of a genetic programming financial forecasting algorithm. Soft Computing [Online] 21:295-310. Available at: http://dx.doi.org/10.1007/s00500-015-1614-8.
Vastardis, N., Kampouridis, M. and Yang, K. (2016). A user behaviour-driven smart-home gateway for energy management. Journal of Ambient Intelligence and Smart Environments [Online] 8:583-602. Available at: http://dx.doi.org/10.3233/AIS-160403.
Kampouridis, M., Otero, F. and Kampouridis, M. (2016). Evolving Trading Strategies Using Directional Changes. Expert Systems with Applications [Online] 73:145-160. Available at: http://dx.doi.org/10.1016/j.eswa.2016.12.032.
Kim, Y. et al. (2016). Discrete Dynamics in Evolutionary Computation and Its Applications. Discrete Dynamics in Nature and Society [Online] 2016:1-2. Available at: https://doi.org/10.1155/2016/6043597.
Kampouridis, M., Alsheddy, A. and Tsang, E. (2013). On the investigation of hyper-heuristics on a financial forecasting problem. Annals of Mathematics and Artificial Intelligence [Online] 68:225-246. Available at: http://dx.doi.org/10.1007/s10472-012-9283-0.
Kampouridis, M., Chen, S. and Tsang, E. (2012). Microstructure Dynamics and Agent-Based Financial Markets: Can Dinosaurs Return? Advances in Complex Systems [Online] 15:1250060. Available at: http://dx.doi.org/10.1142/S0219525912500609.
Kampouridis, M. and Tsang, E. (2012). Investment Opportunities Forecasting: Extending the Grammar of a GP-based Tool. International Journal of Computational Intelligence Systems [Online] 5:530-541. Available at: http://dx.doi.org/10.1080/18756891.2012.696918.
Kampouridis, M., Chen, S. and Tsang, E. (2012). Market Fraction Hypothesis: A proposed test. International Review of Financial Analysis [Online] 23:41-54. Available at: http://dx.doi.org/10.1016/j.irfa.2011.06.009.
Book section
Kampouridis, M., Chen, S. and Tsang, E. (2011). Market Microstructure: A Self-Organizing Map Approach for Investigating Behavior Dynamics under an Evolutionary Environment. in: Natural Computing in Computational Finance,. Springer, pp. 181-197.
Kampouridis, M., Chen, S. and Tsang, E. (2011). The Market Fraction Hypothesis under different GP algorithms. in: Information Systems for Global Financial Markets: Emerging Developments and Effects,. IGI Global, pp. 37-54.
Conference or workshop item
Kampouridis, M., Adegboye, A. and Johnson, C. (2017). Evolving Directional Changes Trading Strategies with a New Event-based Indicator. in: SEAL 2017 : The 11th International Conference on Simulated Evolution and Learning. pp. 727-738. Available at: https://doi.org/10.1007/978-3-319-68759-9_59.
Cramer, S. et al. (2017). Pricing Rainfall Based Futures Using Genetic Programming. in: 20th European Conference, EvoApplications: European Conference on the Applications of Evolutionary Computation. Springer, pp. 17-33. Available at: http://dx.doi.org/10.1007%2F978-3-319-55849-3_2.
Adegboye, A., Kampouridis, M. and Johnson, C. (2017). Regression genetic programming for estimating trend end in foreign exchange market. in: IEEE Symposium Series on Computational Intelligence. Institute of Electrical and Electronics Engineers.
Cramer, S. et al. (2016). Predicting Rainfall in the Context of Rainfall Derivatives Using Genetic Programming. in: IEEE Computational Intelligence for Financial Engineering & Economics, Symposium Series on Computational Intelligence. IEEE, pp. 711-718. Available at: https://doi.org/10.1109/SSCI.2015.108.
Cramer, S., Kampouridis, M. and Freitas, A. (2016). Feature Engineering for Improving Financial Derivatives-based Rainfall Prediction. in: IEEE World Congress on Evolutionary Computation.
Cramer, S., Kampouridis, M. and Freitas, A. (2016). A Genetic Decomposition Algorithm for Predicting Rainfall within Financial Weather Derivatives. in: Genetic and Evolutionary Computation Conference (GECCO 2016).
Cramer, S. and Kampouridis, M. (2015). Optimising the deployment of fibre optics using Guided Local Search. in: IEEE Congress on Evolutionary Computation (CEC).. Available at: http://www.cec2015.org/.
Gypteau, J., Otero, F. and Kampouridis, M. (2015). Generating Directional Change Based Trading Strategies with Genetic Programming. in: Mora, A. M. and Squillero, G. eds. EvoApplications, EvoStar 2015. Springer, pp. 1-12.
Shao, M. et al. (2014). Guided Fast Local Search for Speeding Up a Financial Forecasting Algorithm. in: IEEE Computational Intelligence for Financial Engineering & Economics (CIFEr).
Otero, F. and Kampouridis, M. (2014). A Comparative Study on the Use of Classification Algorithms in Financial Forecasting. in: EvoApplications 2014. Springer-Verlag Berlin, pp. 276-287. Available at: http://dx.doi.org/10.1007/978-3-662-45523-4_23.
Kattan, A. et al. (2014). Transformation of Input Space using Statistical Moments: EA-Based Approach. in: IEEE World Congress on Evolutionary Computation (WCCI).
Aluko, B. et al. (2014). Combining different meta-heuristics to improve the predictability of a financial forecasting algorithm. in: IEEE Computational Intelligence for Financial Engineering & Economics (CIFEr).
Brookhouse, J., Otero, F. and Kampouridis, M. (2014). Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results. in: 16th International Conference on Genetic and Evolutionary Computation (GECCO 2014). pp. 1117-1124. Available at: http://dx.doi.org/10.1145/2598394.2605689.
Kattan, A. and Kampouridis, M. (2014). Generalisation Enhancement via Input Space Transformation: A GP Approach. in: EuroGP 2014,. Springer (Nominated for Best Paper Award), p. to appear.
Kampouridis, M. (2013). An initial investigation of choice function hyper-heuristics for the problem of financial forecasting. in: Evolutionary Computation (CEC), 2013 IEEE Congress on. pp. 2406-2413.
Kampouridis, M. and Otero, F. (2013). Using attribute construction to improve the predictability of a GP financial forecasting algorithm. in: Technologies and Applications of Artificial Intelligence (TAAI 2013). pp. 55-60. Available at: http://dx.doi.org/10.1109/TAAI.2013.24.
Shaghaghi, A. et al. (2013). Guided local search for optimal GPON/FTTP network design. in: Proceedings of the Fourth International Conference on Networks & Communications. Springer.
Kampouridis, M. and Sim, K. (2013). A GP approach for price-speed optimizing negotiation. in: IEEE Congress on Evolutionary Computation (CEC). pp. 1170-1177.
Smonou, D., Kampouridis, M. and Tsang, E. (2013). Metaheuristics Application on a Financial Forecasting Problem. in: IEEE Congress on Evolutionary Computation (IEEE CEC 2013).
Kampouridis, M. et al. (2012). Using a Genetic Algorithm as a Decision Support Tool for the Deployment of Fiber Optic Networks. in: Proceedings of the IEEE World Congress on Computational Intelligence. Brisbane, Australia.
Alsheddy, A. and Kampouridis, M. (2012). Off-line Parameter Tuning for Guided Local Search Using Genetic Programming. in: IEEE World Congress on Evolutionary Computation (WCCI).
Chen, S., Kampouridis, M. and Tsang, E. (2011). Microstructure Dynamics and Agent-Based Financial Markets. in: Multi-Agent-Based Simulation XI, 11th International Workshop, Revised Papers, LNAI. Berlin Heidelberg: Springer-Verlag, pp. 121-135.
Kampouridis, M., Chen, S. and Tsang, E. (2011). Market Microstructure: Can Dinosaurs Return? A Self-Organizing Map Approach under an Evolutionary Framework. in: EvoApplications, EvoStar 2011. pp. 91-100.
Kampouridis, M. and Tsang, E. (2011). Using Hyperheuristics under a GP framework for Financial Forecasting. in: Proc. Fifth International Conference on Learning and Intelligent Optimization (LION5). Springer, Heidelberg, pp. 16-30.
Kampouridis, M., Chen, S. and Tsang, E. (2011). Investigating the Effect of Different GP Algorithms on the Non-Stationary Behavior of Financial Markets. in: Computational Intelligence for Financial Engineering and Economics. IEEE Press.
Chen, S., Kampouridis, M. and Tsang, E. (2010). Microstructure Dynamics and Agent-Based Financial Markets. in: Proceedings of the 11th International Workshop on Multi-Agent-Based Simulation (MABS 2010). Toronto, Canada, pp. 117-128.
Kampouridis, M., Chen, S. and Tsang, E. (2010). Testing the Dinosaur Hypothesis under Empirical Datasets. in: Parallel Problem Solving from Nature — PPSN XI. Springer, pp. 199-208.
Kampouridis, M. and Tsang, E. (2010). EDDIE for Investment Opportunities Forecasting: Extending the Search Space of the GP. in: Proceedings of the IEEE World Congress on Computational Intelligence. Barcelona, Spain, pp. 2019-2026.
Kampouridis, M., Chen, S. and Tsang, E. (2010). Testing the Dinosaur Hypothesis Under Different GP Algorithms. in: Proceedings of the UK Computational Intelligence Workshop (UKCI), IEEE Xplore. Essex, pp. 1-7.
Total publications in KAR: 41 [See all in KAR]

School of Computing, University of Kent, Canterbury, Kent, CT2 7NF

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Last Updated: 15/12/2017