Evolutionary Multiobjective Optimization with Gaussian Process Models

This book focuses on the field of surrogate-model-based multiobjective evolutionary optimization. It describes the sate-of-the-art concepts and methods, presents various optimization problems and describes current challenges. 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.

Autorentext

Miha Mlakar finished his Ph.D. in Information and Communication Technologies from the Joef Stefan International Postgraduate School in Ljubljana, Slovenia.He is currently working as a Postdoctoral Associate at Joef Stefan Insitute, focusing on evolutionary algorithms, optimization, machine learning, data science and industrial applications.