RandOptGen: A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces
Moritz Vinzent Seiler, Oliver Ludger Preuß, Heike Trautmann · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
We propose a versatile problem generator, called RandOptGen, for creating diverse and complex mixed-variable optimization problems, including single- and multi-objective problems. The generator implements a tree-based structure where decision variables from continuous, integer, and categorical domains are transformed into complex objectives using arbitrary mathematical operators. It ensures the feasibility of the generated problems through a validation process by, e.g., verifying that the objective spaces lie within predefined bounds and that multi-objective problems exhibit meaningful trade-offs, characterized by a well-formed Pareto front of the generated multi-objective problems.