This study describes the development of a new binary encoded genetic algorithm for the combinatorial problem of determining a subset of species and their associated reactions that best represent the full starting point reaction mechanism in modelling aviation fuel oxidation. The genetic algorithm has a dual objective in finding a reduced mechanism that best represents aviation fuel oxidation in both a laminar premixed flame and perfectly stirred reactor systems. The number of species in the subset chosen is kept fixed and is specified at the start of the procedure. The genetic algorithm chooses ever improving mechanisms based on an objective function which measures how well the new reduced mechanisms predict a set of species' profiles simulated by the full mechanism. In order to verify the validity of our approach, a full enumeration was performed on a reduced problem and it was found that the genetic algorithm was able to find the optimum solution to this reduced problem after a few generations. The reduction involved going from 338 reactions involving 67 species to 215 reactions involving 50 species. This corresponded to a 90% CPU time saving in each function evaluation. A second step was to take the reduced reaction mechanism and to use a second real encoded genetic algorithm for the parameter optimisation problem of determining the optimal reaction rate parameters that best model an experimental set of premixed flame and jet stirred reactor species' profiles. A significant improvement could be seen in the species profiles obtained using the mechanism with the GA optimised rates over those obtained from the original reduced mechanism. Further, in order to increase the efficiency of the second reaction rate coefficient optimisation step, a new hybrid method was developed which incorporates a direct optimisation method (Rosenbrock method) into the GA. A significant improvement in both accuracy and efficiency was apparent in using this new hybrid approach. (c) 2006 Elsevier Ltd. All rights reserved.