Evolution is a complex process that involves many sources of variation and interactions that make mathematical modeling a challenge. In nature, evolution is a time dependent process that involves a large number of environmental variables influencing the adaptation of a population and its progress. Environmental effects include the interaction between a population with its physical environment, its interaction with other populations and species, and the within population interactions between its members. Accounting for all of these sources of variation in a mathematical model is a challenge. Experimental evolution is no exception, although in such a process the effect of the physical environment is controlled and interactions with other species are limited. Nevertheless, statistical inference of the forces of evolution underlying the process of adaptation in an experimental evolution can be computationally expensive and can involve complicated modeling. In this presentation we propose an Approximate Bayesian Computation (ABC) approach to simplify inference in experimental evolution.