1. Towards Multi-objective Mixed Integer Evolution Strategies
- Author
-
Blom, K. van der, Yang, K., Bäck, T., Emmerich, M.T.M., Deutz, A.H., Hille, S.C., Sergeyev, Y.D., Deutz, A.H., Hille, S.C., and Sergeyev, Y.D.
- Subjects
Mathematical optimization ,Single variable ,Computer science ,Feature (machine learning) ,Integer algorithms ,Optimisation algorithm ,Type (model theory) ,Evolution strategy ,Adaptation (computer science) ,Integer (computer science) - Abstract
Many problems are of a mixed integer nature, rather than being restricted to a single variable type. Although mixed integer algorithms exist for the single-objective case, work on the multi-objective case remains limited. Evolution strategies are stochastic optimisation algorithms that feature step size adaptation mechanisms and are typically used in continuous domains. More recently they were generalised to mixed integer problems. In this work, first steps are taken towards extending the single-objective mixed integer evolution strategy for the multi-objective case. First results are promising, but step size adaptation for the multi-objective case can likely be improved.
- Published
- 2019