Search Results for author:
Found 26 papers, 7 papers with code
On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem
We contribute to the efficient approximation of the Pareto-set for the classical $\mathcal{NP}$-hard multi-objective minimum spanning tree problem (moMST) adopting evolutionary computation.
On the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments.
Runtime Analysis of Quality Diversity Algorithms
Quality diversity~(QD) is a branch of evolutionary computation that gained increasing interest in recent years.
Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem
Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem.
Exploring the Feature Space of TSP Instances Using Quality Diversity
Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem.
Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation
In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown.
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem
In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges.
Generating Instances with Performance Differences for More Than Just Two Algorithms
In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa.
Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem
Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years.
Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms
In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution.
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems
Submodular functions allow to model many real-world optimisation problems.
Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem
In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization.
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem
In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS).
Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions
The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems.
Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
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).
More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization
We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization.
Dynamic Bi-Objective Routing of Multiple Vehicles
In practice, e. g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests.
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection
The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems.
Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem
We consider the Minimum Spanning Tree (MST) problem in a single- and multi-objective version, and introduce a biased mutation, which puts more emphasis on the selection of edges of low rank in terms of low domination number.
Evolving Diverse Sets of Tours for the Travelling Salesperson Problem
Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years.
Initial Design Strategies and their Effects on Sequential Model-Based Optimization
Most works, however, focus on the choice of the model, the acquisition function, and the strategy used to optimize the latter.
The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics
In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour.
One-Shot Decision-Making with and without Surrogates
We study three different decision tasks: classic one-shot optimization (only the best sample matters), one-shot optimization with surrogates (allowing to use surrogate models for selecting a design that need not necessarily be one of the evaluated samples), and one-shot regression (i. e., function approximation, with minimization of mean squared error as objective).
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model.
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks.
