1 code implementation • 2 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.
no code implementations • 28 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.
no code implementations • 28 Jul 2022 • Adel Nikfarjam, Amirhossein Moosavi, Aneta Neumann, Frank Neumann
Diversification in a set of solutions has become a hot research topic in the evolutionary computation community.
no code implementations • 23 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.
no code implementations • 12 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.
no code implementations • 12 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.
no code implementations • 7 Apr 2022 • Diksha Goel, Max Ward, Aneta Neumann, Frank Neumann, Hung Nguyen, Mingyu Guo
We show that the problem is #P-hard and, therefore, intractable to solve exactly.
no code implementations • 6 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.
1 code implementation • 25 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.
no code implementations • 25 Dec 2021 • Mingyu Guo, Jialiang Li, Aneta Neumann, Frank Neumann, Hung Nguyen
The other assumes a small number of splitting nodes (nodes with multiple out-going edges).
no code implementations • 16 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.
no code implementations • 11 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.
no code implementations • 28 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.
1 code implementation • 27 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.
no code implementations • 8 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.
no code implementations • 23 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).
no code implementations • 10 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.
no code implementations • 10 Feb 2021 • William Reid, Aneta Neumann, Simon Ratcliffe, Frank Neumann
In this paper, we investigate the impact of uncertainty in advanced ore mine optimisation.
no code implementations • 10 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.
no code implementations • 22 Oct 2020 • Aneta Neumann, Jakob Bossek, Frank Neumann
Submodular functions allow to model many real-world optimisation problems.
no code implementations • 20 Jun 2020 • Aneta Neumann, Frank Neumann
We show that the GSEMO algorithm obtains the same worst case performance guarantees as recently analyzed greedy algorithms.
no code implementations • 5 Jun 2020 • Jakob Bossek, Aneta Neumann, Frank Neumann
The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems.
no code implementations • 27 Apr 2020 • Vahid Roostapour, Aneta Neumann, Frank Neumann
Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments.
no code implementations • 20 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.
no code implementations • 7 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.
no code implementations • 2 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.
no code implementations • 17 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.
1 code implementation • 19 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).
no code implementations • 26 Nov 2019 • Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton
In this paper, we investigate submodular optimization problems with chance constraints.
no code implementations • 15 Nov 2019 • Vanja Doskoč, Tobias Friedrich, Andreas Göbel, Frank Neumann, Aneta Neumann, Francesco Quinzan
We show that our proposed algorithm competes with the state-of-the-art in static settings.
no code implementations • 13 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.
no code implementations • 16 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.
no code implementations • 14 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.
no code implementations • 15 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.
no code implementations • 15 Feb 2018 • Aneta Neumann, Wanru Gao, Carola Doerr, Frank Neumann, Markus Wagner
Diversity plays a crucial role in evolutionary computation.
no code implementations • 10 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.
no code implementations • 5 Aug 2016 • Aneta Neumann, Bradley Alexander, Frank Neumann
Evolutionary algorithms have been used in many ways to generate digital art.
no code implementations • 21 Apr 2016 • Aneta Neumann, Bradley Alexander, Frank Neumann
Evolutionary algorithms have been widely studied from a theoretical perspective.