Natural Selection of Asphalt Mix Stiffness Predictive Models with Genetic Programming

Genetic Programming (GP) is a systematic, domain-independent evolutionary computation technique that stochastically evolves populations of computer programs to perform a user-defined task. Similar to Genetic Algorithms (GA) which evolves a population of individuals to better ones, GP iteratively transforms a population of computer programs into a new generation of programs by applying biologically inspired operations such as crossover, mutation, etc. In this paper, a population of Hot-Mix Asphalt (HMA) dynamic modulus stiffness prediction models is genetically evolved to better ones by applying the principles of genetic programming. The HMA dynamic modulus (|E*|), one of the stiffness measures, is the primary HMA material property input in the new Mechanistic Empirical Pavement Design Guide (MEPDG) developed under National Cooperative Highway Research Program (NCHRP) 1-37A (2004) for the American State Highway and Transportation Officials (AASHTO). It is shown that the evolved HMA model through GP is reasonably compact and contains both linear terms and low-order non-linear transformations of input variables for simplification.