2022/08/23 by Henri Thölke, Thölke, Henri, Jens Kosiol +1
Computer Science · #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Software Engineering (cs.SE) #cs.NE #cs.SE
paper · pdf · doi:10.48550/arxiv.2208.10881
13 pages, 3 figures; accepted for publication at the 2022 edition of the workshop on Model Driven Engineering, Verification and Validation (MoDeVVa)
arxiv created 2022/08/23 · arxiv updated 2022/08/24
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.