Landscape-aware performance prediction for evolutionary multiobjective optimization
We expose and contrast the impact of landscape characteristics on the performance of
search heuristics for black-box multiobjective combinatorial optimization problems. A sound …
search heuristics for black-box multiobjective combinatorial optimization problems. A sound …
Automated algorithm selection: Survey and perspectives
It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …
intensely studied, different instances are best solved using different algorithms. This is …
Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning
P Kerschke, H Trautmann - Evolutionary computation, 2019 - direct.mit.edu
In this article, we build upon previous work on designing informative and efficient
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
Comprehensive feature-based landscape analysis of continuous and constrained optimization problems using the R-package flacco
P Kerschke, H Trautmann - … in Statistical Computing: From Music Data …, 2019 - Springer
Choosing the best-performing optimizer (s) out of a portfolio of optimization algorithms is
usually a difficult and complex task. It gets even worse, if the underlying functions are …
usually a difficult and complex task. It gets even worse, if the underlying functions are …
Searching the landscape of flux vacua with genetic algorithms
A bstract In this paper, we employ genetic algorithms to explore the landscape of type IIB flux
vacua. We show that genetic algorithms can efficiently scan the landscape for viable …
vacua. We show that genetic algorithms can efficiently scan the landscape for viable …
Evolutionary black-box topology optimization: Challenges and promises
Black-box topology optimization (BBTO) uses evolutionary algorithms and other soft
computing techniques to generate near-optimal topologies of mechanical structures …
computing techniques to generate near-optimal topologies of mechanical structures …
Adaptive recommendation model using meta-learning for population-based algorithms
To efficiently solve complex optimization problems, numerous population-based meta-
heuristics and extensions have been developed. However, the performances of the …
heuristics and extensions have been developed. However, the performances of the …
A survey of genetic improvement search spaces
Genetic Improvement (GI) uses automated search to improve existing software. Most GI work
has focused on empirical studies that successfully apply GI to improve software's running …
has focused on empirical studies that successfully apply GI to improve software's running …
Kernelized evolutionary distance metric learning for semi-supervised clustering
This study proposes a novel distance metric learning method called evolutionary distance
metric learning (EDML) to improve clustering quality that simultaneously evaluates inter-and …
metric learning (EDML) to improve clustering quality that simultaneously evaluates inter-and …
Classification of permutation distance metrics for fitness landscape analysis
VA Cicirello - … Technologies: 11th EAI International Conference, BICT …, 2019 - Springer
Commonly used computational and analytical tools for fitness landscape analysis of
optimization problems require identifying a distance metric that characterizes the similarity of …
optimization problems require identifying a distance metric that characterizes the similarity of …