Search Results for author: Günter Rudolph

Found 6 papers, 0 papers with code

Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization

no code implementations19 Jun 2024 Frank Neumann, Günter Rudolph

Constrained submodular optimization problems play a key role in the area of combinatorial optimization as they capture many NP-hard optimization problems.

Combinatorial Optimization Evolutionary Algorithms

Towards Decision Support in Dynamic Bi-Objective Vehicle Routing

no code implementations28 May 2020 Jakob Bossek, Christian Grimme, Günter Rudolph, Heike Trautmann

Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras).

Decision Making Diversity

An Empirical Approach For Probing the Definiteness of Kernels

no code implementations10 Jul 2018 Martin Zaefferer, Thomas Bartz-Beielstein, Günter Rudolph

We provide a proof-of-concept with 16 different distance measures for permutations.

Surrogate-Assisted Partial Order-based Evolutionary Optimisation

no code implementations1 Nov 2016 Vanessa Volz, Günter Rudolph, Boris Naujoks

In this paper, we propose a novel approach (SAPEO) to support the survival selection process in multi-objective evolutionary algorithms with surrogate models - it dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population.

Evolutionary Algorithms

Demonstrating the Feasibility of Automatic Game Balancing

no code implementations11 Mar 2016 Vanessa Volz, Günter Rudolph, Boris Naujoks

In this paper, the feasibility of automatic balancing using simulation- and deck-based objectives is investigated for the card game top trumps.

Fairness Game Design +1

Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms

no code implementations26 Mar 2015 Luis Marti, Christian Grimme, Pascal Kerschke, Heike Trautmann, Günter Rudolph

Therefore, we propose a postprocessing strategy which consists of applying the averaged Hausdorff indicator to the complete archive of generated solutions after optimization in order to select a uniformly distributed subset of nondominated solutions from the archive.

Multiobjective Optimization

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