Evolutionary Multiobjective Optimization Based on Gaussian Process Modeling

Miha Mlakar


This paper presents a summary of the doctoral dissertation of the author, which addresses the task of evolutionary multiobjective optimization using surrogate models. The main contributions are done for the optimization problems, where solutions are presented with uncertainty. To compare solutions under uncertainty and improve the optimization results the new relations for comparing solutions under uncertainty are defined. These relations reduce the possibility of incorrect comparisons due to the inaccurate approximations. The relations under uncertainty are then used in the new surrogate-model-based multiobjective evolutionary algorithm called GP-DEMO. The algorithm is thoroughly tested on benchmark and real-world problems and the results show that GP-DEMO, in comparison to other multiobjective evolutionary algorithms, produces comparable results while requiring fewer exact evaluations of the original objective functions.

Full Text:



Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Wiley, New York.

Mlakar, Miha. Evolutionary multiobjective optimization based on Gaussian process, PhD Thesis, IPS Jožef Stefan, Ljubljana, Slovenia, April, 2015

Mlakar, M., Tušar, T., & Filipič, B. Comparing solutions under uncertainty in multiobjective optimization. Mathematical Problems in Engineering, 2014. doi:10 .1155/2014/817964

Mlakar, M., Petelin, D., Tušar, T., & Filipič, B. GP-DEMO: Differential evolution for multiobjective optimization based on Gaussian process models. European Journal of Operational Research, 243 (2), 347–361

T. Robič and B. Filipič, DEMO: Differential evolution for multiobjective optimization, in Proc. of 3rd Int. Conf. Evol. Multi-Criterion Optimization, 2005, pp. 520–533.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.