Category Archives: Sequential Parameter Optimization

#Surrogate #Metamodels, and #MachineLearning article online: Model-based Methods for Continuous and Discrete Global #Optimization

http://www.sciencedirect.com/science/article/pii/S1568494617300546

http://www.sciencedirect.com/science/article/pii/S1568494617300546

The article “Model-based Methods for Continuous and Discrete Global Optimization” by T. Bartz-Beielstein and M. Zaefferer is available online: http://www.sciencedirect.com/science/article/pii/S1568494617300546
A preprint can be downloaded from “Cologne Open Science”: urn:nbn:de:hbz:832-cos4-4356
Abstract:
The use of surrogate models is a standard method for dealing with complex real-world optimization problems. The first surrogate models were applied to continuous optimization problems. In recent years, surrogate models gained importance for discrete optimization problems. This article takes this development into consideration. The first part presents a survey of model-based methods, focusing on continuous optimization. It introduces a taxonomy, which is useful as a guideline for selecting adequate model-based optimization tools. The second part examines discrete optimization problems. Here, six strategies for dealing with discrete data structures are introduced. A new approach for combining surrogate information via stacking is proposed in the third part. The implementation of this approach will be available in the open source R package SPOT2. The article concludes with a discussion of recent developments and challenges in continuous and discrete application domains.

Bachelorarbeit von Y. Fu @th_koeln: “Untersuchungen und mathematische Analyse von Piezosensoren zur Anwendung im Structural Health Monitoring für Faserverbundstrukturen”

Versuchsaufbau und Sensorsignal. Quelle: Y. Fu

Yixi Fu hat im SPOTSeven Lab Kolloquium am 6.2.2016 ihre Bachelorarbeit “Untersuchungen und mathematische Analyse von Piezosensoren zur Anwendung im Structural Health Monitoring für Faserverbundstrukturen” vorgestellt. Die Arbeit wurde gemeinsam von den Professoren Blaurock und Bartz-Beielstein betreut.

Model-based Methods for Continuous and Discrete Global Optimization

The article “Model-based Methods for Continuous and Discrete Global Optimization” by Thomas Bartz-Beielstein and Martin Zaefferer  will be published in Applied Soft Computing Journal. It will be available soon.

The first part of this article presents a survey of model-based methods, focusing on continuous optimization. It introduces a taxonomy, which is useful as a guideline for selecting adequate model-based optimization tools. The second part provides details for the case of discrete optimization problems. Here, six strategies for dealing with discrete data structures are introduced. A new approach for combining surrogate information via stacking is proposed in the third part.

New Paper: An Adaptive Memetic Algorithm for the Architecture Optimisation Problem

ArcheOpterix is a generic platform for modelling, evaluating and optimising embedded systems. The main modules of ArcheOpterix are shown in the figure, which was taken from: http://users.monash.edu.au/~aldeidaa/ArcheOpterix.html

Nasser R. Sabar‘s  (Queensland University of Technology, Brisbane) and Aldeida Aleti‘s (Faculty of Information Technology, Monash University, Melbourne) paper “An Adaptive Memetic Algorithm for the Architecture Optimisation Problem” [1] describes the combination of  a local search and a genetic algorithm to generate memetic algorithms. Continue reading