Domain-driven Correlation-aware Recombination and Mutation Operators for Complex Real-world Applications

Abstract

Evolutionary algorithms are a very general method for optimization problems that allow adaption to many different use cases. Application to real-world problems usually comes with features as constraints, dependencies and approximations. When a multidimensional search space comes with strings attached- namely dependencies between its dimensions- an expression in two ways is possible: Restrictive-as equalities or inequalities- or vague-as correlations between dimensions, for example. Correlations between dimensions are not as easy to grasp as constraints. Therefore, well-known techniques as death penalty or penalty functions do not apply directly. We propose new mutation and recombination operators that incorporate domain knowledge to increase the offspring fraction that adheres to these correlations. We evaluate our approach with several benchmark functions and different assumptions on the dependencies of the search space. We compare the likelihood of valid (in terms of adhering correlations) outcomes of algorithms using standard mutation and recombination operators to those with the proposed operators. We find that the correlation-aware operators preserve population's features in terms of dependencies.