Search Results for author: Aneta Neumann

Found 27 papers, 2 papers with code

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.

Analysis of Evolutionary Diversity Optimisation for Permutation Problems

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

Generating diverse populations of high quality solutions has gained interest as a promising extension to the traditional optimization tasks.

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.

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.

Evolutionary Multi-Objective Optimization for the Dynamic Knapsack Problem

no code implementations27 Apr 2020 Vahid Roostapour, Aneta Neumann, Frank Neumann

Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments.

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

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.

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