Finding Semi-Quantitative Physical Models Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{khoury:2006:UKCI,

• author = "Mehdi Khoury and Frank Guerin and George Macleod Coghill",
• title = "Finding Semi-Quantitative Physical Models Using Genetic Programming",
• booktitle = "The 6th annual UK Workshop on Computational Intelligence",
• year = "2006",
• editor = "Xue Z. Wang and Rui Fa Li",
• pages = "245--252",
• address = "Leeds, UK",
• month = "4-6 " # sep,
• keywords = "genetic algorithms, genetic programming, fuzzy, qualitative modelling, semi quantitative modelling",
• URL = "http://www.csd.abdn.ac.uk/~mkhoury/fuzzy%20evolution2.pdf",
• size = "8 pages",
• abstract = "Model learning often implies exploring a vast search space of possible hypotheses in the hope of finding a solution. Qualitative model learners are mostly based on Inductive Logic Programming (ILP), which is a systematic method which tends to be well fitted for exploring solutions in a narrow search space. We present a semi-quantitative model learner that uses Genetic Programming (GP), which is well suited for exploring a broad search space. We learn simple physical systems based on a formalism involving both crisp numbers and fuzzy quantity spaces. We use the ECJ framework,1 and the fitness of a model is set to be optimal when it covers all positive examples. Several experiments are performed to learn and reuse models of physical systems of increasing complexity; firstly a u-tube, then coupled tanks, and finally cascading tanks. Results show that the system can approximate the target models in reasonably good conditions, and that there is still scope for optimisation.",

}

Genetic Programming entries for Mehdi Khoury Frank Guerin George M Coghill

Citations