Search Results for author: Aneta Neumann

Found 38 papers, 4 papers with code

Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler

1 code implementation2 Feb 2023 Frank Neumann, Aneta Neumann, Chao Qian, Viet Anh Do, Jacob de Nobel, Diederick Vermetten, Saba Sadeghi Ahouei, Furong Ye, Hao Wang, Thomas Bäck

Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns.

Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem

no code implementations28 Jul 2022 Adel Nikfarjam, Aneta Neumann, Jakob Bossek, Frank Neumann

Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem.

Evolutionary Time-Use Optimization for Improving Children's Health Outcomes

no code implementations23 Jun 2022 Yue Xie, Aneta Neumann, Ty Stanford, Charlotte Lund Rasmussen, Dorothea Dumuid, Frank Neumann

We then investigate the performance of evolutionary algorithms to optimize time use for four individual health outcomes with hypothetical children with different day structures.

Coevolutionary Pareto Diversity Optimization

no code implementations12 Apr 2022 Aneta Neumann, Denis Antipov, Frank Neumann

Our new Pareto Diversity optimization approach uses this bi-objective formulation to optimize the problem while also maintaining an additional population of high quality solutions for which diversity is optimized with respect to a given diversity measure.

Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits

no code implementations12 Apr 2022 Aneta Neumann, Yue Xie, Frank Neumann

We examine simple evolutionary algorithms and the use of heavy tail mutation and a problem-specific crossover operator for optimizing uncertain profits.

Stochastic Optimization

Evolutionary Diversity Optimisation for The Traveling Thief Problem

no code implementations6 Apr 2022 Adel Nikfarjam, Aneta Neumann, Frank Neumann

There has been a growing interest in the evolutionary computation community to compute a diverse set of high-quality solutions for a given optimisation problem.

Niching-based Evolutionary Diversity Optimization for the Traveling Salesperson Problem

1 code implementation25 Jan 2022 Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann

In this work, we consider the problem of finding a set of tours to a traveling salesperson problem (TSP) instance maximizing diversity, while satisfying a given cost constraint.

On the Use of Quality Diversity Algorithms for The Traveling Thief Problem

no code implementations16 Dec 2021 Adel Nikfarjam, Aneta Neumann, Frank Neumann

In real-world optimisation, it is common to face several sub-problems interacting and forming the main problem.

Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation

no code implementations11 Aug 2021 Adel Nikfarjam, Jakob Bossek, Aneta Neumann, Frank Neumann

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.

Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem

no code implementations28 Apr 2021 Adel Nikfarjam, Jakob Bossek, Aneta Neumann, Frank Neumann

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

1 code implementation27 Apr 2021 Jakob Bossek, Aneta Neumann, Frank Neumann

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.

Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems

no code implementations8 Apr 2021 Yue Xie, Aneta Neumann, Frank Neumann

Besides, we introduce a multi-component fitness function for solving the large-scale stockpile blending problem which can maximize the volume of metal over the plan and maintain the balance between stockpiles according to the usage of metal.

Scheduling

Analysis of Evolutionary Diversity Optimisation for Permutation Problems

no code implementations23 Feb 2021 Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann

This work contributes to this line of research with an investigation on evolutionary diversity optimization for three of the most well-studied permutation problems, namely the Traveling Salesperson Problem (TSP), both symmetric and asymmetric variants, and Quadratic Assignment Problem (QAP).

Runtime Analysis of RLS and the (1+1) EA for the Chance-constrained Knapsack Problem with Correlated Uniform Weights

no code implementations10 Feb 2021 Yue Xie, Aneta Neumann, Frank Neumann, Andrew M. Sutton

We perform runtime analysis of a randomized search algorithm (RSA) and a basic evolutionary algorithm (EA) for the chance-constrained knapsack problem with correlated uniform weights.

Advanced Ore Mine Optimisation under Uncertainty Using Evolution

no code implementations10 Feb 2021 William Reid, Aneta Neumann, Simon Ratcliffe, Frank Neumann

In this paper, we investigate the impact of uncertainty in advanced ore mine optimisation.

Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints

no code implementations10 Feb 2021 Yue Xie, Aneta Neumann, Frank Neumann

In this paper, we consider the uncertainty in material grades and introduce chance constraints that are used to ensure the constraints with high confidence.

Scheduling

Optimising Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms

no code implementations20 Jun 2020 Aneta Neumann, Frank Neumann

We show that the GSEMO algorithm obtains the same worst case performance guarantees as recently analyzed greedy algorithms.

Evolving Diverse Sets of Tours for the Travelling Salesperson Problem

no code implementations20 Apr 2020 Anh Viet Do, Jakob Bossek, Aneta Neumann, Frank Neumann

Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years.

Specific Single- and Multi-Objective Evolutionary Algorithms for the Chance-Constrained Knapsack Problem

no code implementations7 Apr 2020 Yue Xie, Aneta Neumann, Frank Neumann

We use this model in combination with the problem-specific crossover operator in multi-objective evolutionary algorithms to solve the problem.

Evolutionary Image Transition and Painting Using Random Walks

no code implementations2 Mar 2020 Aneta Neumann, Bradley Alexander, Frank Neumann

We introduce an evolutionary image painting approach whose underlying biased random walk can be controlled by a parameter influencing the bias of the random walk and thereby creating different artistic painting effects.

Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives

no code implementations17 Feb 2020 Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann, Frank Neumann

In this paper, we consider the dynamic chance-constrained knapsack problem where the weight of each item is stochastic, the capacity constraint changes dynamically over time, and the objective is to maximize the total profit subject to the probability that total weight exceeds the capacity.

Combinatorial Optimization

One-Shot Decision-Making with and without Surrogates

1 code implementation19 Dec 2019 Jakob Bossek, Pascal Kerschke, Aneta Neumann, Frank Neumann, Carola Doerr

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).

Decision Making regression

Optimization of Chance-Constrained Submodular Functions

no code implementations26 Nov 2019 Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton

In this paper, we investigate submodular optimization problems with chance constraints.

Evolutionary Algorithms for the Chance-Constrained Knapsack Problem

no code implementations13 Feb 2019 Yue Xie, Oscar Harper, Hirad Assimi, Aneta Neumann, Frank Neumann

In the experiment section, we evaluate and compare the deterministic approaches and evolutionary algorithms on a wide range of instances.

Stochastic Optimization

Evolutionary Diversity Optimization Using Multi-Objective Indicators

no code implementations16 Nov 2018 Aneta Neumann, Wanru Gao, Markus Wagner, Frank Neumann

Evolutionary diversity optimization aims to compute a diverse set of solutions where all solutions meet a given quality criterion.

Pareto Optimization for Subset Selection with Dynamic Cost Constraints

no code implementations14 Nov 2018 Vahid Roostapour, Aneta Neumann, Frank Neumann, Tobias Friedrich

We also consider EAMC, a new evolutionary algorithm with polynomial expected time guarantee to maintain $\phi$ approximation ratio, and NSGA-II with two different population sizes as advanced multi-objective optimization algorithm, to demonstrate their challenges in optimizing the maximum coverage problem.

Evolution of Images with Diversity and Constraints Using a Generator Network

no code implementations15 Feb 2018 Aneta Neumann, Christo Pyromallis, Bradley Alexander

To date this work has focused on the generation of images concordant with one or more classes and transfer of artistic styles.

Evolutionary Image Composition Using Feature Covariance Matrices

no code implementations10 Mar 2017 Aneta Neumann, Zygmunt L. Szpak, Wojciech Chojnacki, Frank Neumann

This approach is very flexible in that it can work with a wide range of features and enables targeting specific regions in the images.

Evolutionary Image Transition Based on Theoretical Insights of Random Processes

no code implementations21 Apr 2016 Aneta Neumann, Bradley Alexander, Frank Neumann

Evolutionary algorithms have been widely studied from a theoretical perspective.

Cannot find the paper you are looking for? You can Submit a new open access paper.