no code implementations • 18 Apr 2024 • Xiankun Yan, Aneta Neumann, Frank Neumann

Its results are compared with those from other algorithms using different surrogate functions.

no code implementations • 17 Apr 2024 • Jonathan Gadea Harder, Aneta Neumann, Frank Neumann

For complete bipartite graphs, our runtime analysis shows that, with a reasonably small $\mu$, the $(\mu+1)$-EA achieves maximal diversity with an expected runtime of $O(\mu^2 m^4 \log(m))$ for the small gap case (where the population size $\mu$ is less than the difference in the sizes of the bipartite partitions) and $O(\mu^2 m^2 \log(m))$ otherwise.

no code implementations • 17 Apr 2024 • Denis Antipov, Aneta Neumann, Frank Neumann, Andrew M. Sutton

The diversity optimization is the class of optimization problems, in which we aim at finding a diverse set of good solutions.

no code implementations • 12 Apr 2024 • Kokila Kasuni Perera, Aneta Neumann

The chance-constrained problem model allows us to effectively capture the stochastic profits and associate a confidence level to the solutions' profits.

no code implementations • 9 Apr 2024 • Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann

We introduce a 3-objective formulation that is able to deal with the stochastic and dynamic components at the same time and is independent of the confidence level required for the constraint.

no code implementations • 4 Apr 2024 • Benjamin Doerr, Joshua Knowles, Aneta Neumann, Frank Neumann

We consider whether conditions exist under which block-coordinate descent is asymptotically efficient in evolutionary multi-objective optimization, addressing an open problem.

no code implementations • 15 Jan 2024 • Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann

Creating diverse sets of high quality solutions has become an important problem in recent years.

no code implementations • 14 Jul 2023 • Denis Antipov, Aneta Neumann, Frank Neumann

The evolutionary diversity optimization aims at finding a diverse set of solutions which satisfy some constraint on their fitness.

no code implementations • 30 May 2023 • Jakob Bossek, Aneta Neumann, Frank Neumann

Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments.

no code implementations • 29 May 2023 • Tobias Friedrich, Timo Kötzing, Aneta Neumann, Frank Neumann, Aishwarya Radhakrishnan

Understanding how evolutionary algorithms perform on constrained problems has gained increasing attention in recent years.

no code implementations • 29 May 2023 • Michael Stimson, William Reid, Aneta Neumann, Simon Ratcliffe, Frank Neumann

The new method discounts profits based on uncertainty within an evolutionary algorithm, sacrificing economic optimality of a single geological model for improving the downside risk over an ensemble of equally likely models.

no code implementations • 8 Apr 2023 • Diksha Goel, Aneta Neumann, Frank Neumann, Hung Nguyen, Mingyu Guo

The defender picks a specific environment configuration.

no code implementations • 8 Mar 2023 • Furong Ye, Frank Neumann, Jacob de Nobel, Aneta Neumann, Thomas Bäck

Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms.

no code implementations • 3 Mar 2023 • Kokila Perera, Aneta Neumann, Frank Neumann

We consider a version of the knapsack problem with stochastic profits to guarantee a certain level of confidence in the profit of the solutions.

no code implementations • 25 Feb 2023 • Mingyu Guo, Jialiang Li, Aneta Neumann, Frank Neumann, Hung Nguyen

Given a source s and a destination t, we aim to test s-t connectivity by identifying either a path (consisting of only On edges) or a cut (consisting of only Off edges).

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, 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 • 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 • 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, Wanru Gao, Carola Doerr, Frank Neumann, Markus Wagner

Diversity plays a crucial role in evolutionary computation.

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 • 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.

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

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.