Michael T. M. Emmerich

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The hypervolume measure is one of the most frequently applied measures for comparing the results of evolutionary multiobjective optimization algorithms (EMOA). The idea to use this measure for selection is self-evident. A steady-state EMOA will be devised, that combines concepts of non-dominated sorting with a selection operator based on the hypervolume(More)
This paper presents and analyzes in detail an efficient search method based on Evolutionary Algorithms (EA) assisted by local Gaussian Random Field Metamodels (GRFM). It is created for the use in optimization problems with computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions(More)
This paper presents various Metamodel–Assisted Evolution Strategies which reduce the computational cost of optimisation problems involving time–consuming function evaluations. The metamodel is built using previously evaluated solutions in the search space and utilized to predict the fitness of new candidate solutions. In addition to previous works by the(More)
While the motivation and usefulness of niching methods is beyond doubt, the relaxation of assumptions and limitations concerning the hypothetical search landscape is much needed if niching is to be valid in a broader range of applications. Upon the introduction of radii-based niching methods with derandomized evolution strategies (ES), the purpose of this(More)
Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate(More)
The optimization of chemical engineering plants is still a challenging task. Economical evaluations of a process owsheet using rigorous simulation models are very time consuming. Furthermore, many di erent types of parameters can be involved into the optimization procedure, resulting in highly restricted mixed-integer nonlinear objective functions.(More)