A multiplicity-preserving crossover operator on graphs. Extended version
Abstract
Evolutionary algorithms usually explore a search space of solutions by means of crossover and mutation. While a mutation consists of a small, local modification of a solution, crossover mixes the genetic information of two solutions to compute a new one. For model-driven optimization (MDO), where models directly serve as possible solutions (instead of first transforming them into another representation), only recently a generic crossover operator has been developed. Using graphs as a formal foundation for models, we further refine this operator in such a way that additional well-formedness constraints are preserved: We prove that, given two models that satisfy a given set of multiplicity constraints as input, our refined crossover operator computes two new models as output that also satisfy the set of constraints.
Cite
@article{arxiv.2208.10881,
title = {A multiplicity-preserving crossover operator on graphs. Extended version},
author = {Henri Thölke and Jens Kosiol},
journal= {arXiv preprint arXiv:2208.10881},
year = {2022}
}
Comments
13 pages, 3 figures; accepted for publication at the 2022 edition of the workshop on Model Driven Engineering, Verification and Validation (MoDeVVa)