11 citations · 20 across the 10 of their papers we have counts for
10 papers
On the Complexity of Minimum Riesz s-Energy Subset Selection in Euclidean and Ultrametric Spaces
Michael T. M. Emmerich, Ksenia Pereverdieva, André Deutz
We study the computational complexity of exact cardinality-constrained minimum Riesz -energy subset selection in finite metric spaces: given points, select points of m…
Maximum Solow--Polasky Diversity Subset Selection Is NP-hard Even in the Euclidean Plane
Michael T. M. Emmerich, Ksenia Pereverdieva, André H. Deutz
We prove that, for every fixed , selecting a subset of prescribed cardinality that maximizes the Solow--Polasky diversity indicator is NP-hard for finite point sets in $\mat…
Selecting a Maximum Solow-Polasky Diversity Subset in General Metric Spaces Is NP-hard
Michael T. M. Emmerich, Ksenia Pereverdieva, André H. Deutz
The Solow--Polasky diversity indicator (or magnitude) is a classical measure of diversity based on pairwise distances. It has applications in ecology, conservation planning, and, m…
Comparative Analysis of Indicators for Multiobjective Diversity Optimization
Ksenia Pereverdieva, André Deutz, Tessa Ezendam +3
Indicator-based (multiobjective) diversity optimization aims at finding a set of near (Pareto-)optimal solutions that maximizes a diversity indicator, where diversity is typically…
Multicriteria Optimization and Decision Making: Principles, Algorithms and Case Studies
Michael Emmerich, André Deutz
Real-world decision and optimization problems, often involve constraints and conflicting criteria. For example, choosing a travel method must balance speed, cost, environmental foo…
A Newton Method for Hausdorff Approximations of the Pareto Front within Multi-objective Evolutionary Algorithms
Hao Wang, Angel E. Rodriguez-Fernandez, Lourdes Uribe +3
A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While…
The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity
André H. Deutz, Michael T. M. Emmerich, Hao Wang
The problem of approximating the Pareto front of a multiobjective optimization problem can be reformulated as the problem of finding a set that maximizes the hypervolume indicator.…
Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones
Yali Wang, André Deutz, Thomas Bäck +1
Given a point in -dimensional objective space, any -ball of a point can be partitioned into the incomparable, the dominated and dominating region. The ratio between…
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms
Kaifeng Yang, Michael Emmerich, André Deutz +1
In the field of multi-objective optimization algorithms, multi-objective Bayesian Global Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective op…
Faster Computation of Expected Hypervolume Improvement
Iris Hupkens, Michael Emmerich, André Deutz
The expected improvement algorithm (or efficient global optimization) aims for global continuous optimization with a limited budget of black-box function evaluations. It is based o…