On Translating MiniZinc Constraint Models into Fitness Functions for Evolutionary Algorithms: Application to Continuous Placement Problems

MiniZinc is a solver-independent constraint modeling language which is increasingly used in the constraint programming community. It can be used to compare different solvers which are currently based on either constraint programming, Boolean satisfiability or mixed integer linear programming. In this paper we show how MiniZinc models can be compiled into fitness functions for evolutionary algorithms. More specifically, we describe the translation of FlatZinc models into fitness functions over the reals and their use in the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) solver. We illustrate this approach, and evaluate it, on the modeling and solving of complex shape continuous placement problems.