Aneta Neumann

Dr Aneta Neumann

Researcher

School of Computer Science

Faculty of Engineering, Computer and Mathematical Sciences

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.


Aneta Neumann is a postdoctoral researcher at the School of Computer Science, the University of Adelaide within the Research Consortium: Unlocking Complex Resources through Lean Processing, led by Professor Nigel Cook.

She graduated in Computer Science from the Christian-Albrechts-University of Kiel, Germany and received her PhD from the University of Adelaide, Australia. She was a participant in the SALA 2016 -2018 exhibitions in Adelaide and has presented invited talks at UCL London, Goldsmiths, University of London, the University of Nottingham, the University of Sheffield, Hasso Plattner Institut University Potsdam, Sorbonne University and University of Melbourne in 2016-2019. Aneta is a co-designer and co-lecturer for the EdX Big Data Fundamentals course in the Big Data MicroMasters® program. She received an ACM Women scholarship, sponsored by Google, Microsoft, and Oracle, a Hans-Juergen and Marianna Ohff Research Grant in 2018, and the Best Paper Nomination at GECCO 2019 in the track “Genetic Algorithms”.

Her main research interests include bio-inspired computation, particularly dynamic and stochastic optimisation, submodular functions, diversity, and optimisation under uncertainty in practice. Moreover, her work contributes to understanding the fundamental link between bio-inspired computation, machine learning, and computational creativity. She investigates evolutionary image transition and animation in the area of Artificial Intelligence and examines how to develop designs and applications of artificial intelligent methods based on complex agent-based models.

News: Research Projects, Honours, Masters and PhD Student Applications 2020

Project title: Evolutionary Diversity Optimisation. Project description: The student will be involved in a research project funded by the Australian Research Council on Evolutionary Diversity Optimisation. The goal is to design and analyse evolutionary algorithms for computing a diverse set of high quality solutions. A strong background on algorithms and strong programming skills are required. Knowledge in the area of evolutionary computation is beneficial, but not a requirement. The student will be closely working with other researchers in the Optimisation and Logistics group and the aim would be to write a conference paper based on the outcomes of this summer project. We are able to offer financial assistance for undergraduate students at the UofA.

Project title: Artificial Intelligence - Innovative approaches for increasing the productivity of South Australia’s copper and gold production. Project description: Artificial Intelligence is currently used in various ways to solve significant industry challenges. The students will develop advanced technologies to help boost South Australia’s copper and gold production. The topic spans from experimental investigations of algorithms to data analysis using machine learning methods. The projects can be carried out dependent on the background and interest of the students. We are able to offer financial assistance for female students at the UofA.

Project title: AI-based Computational Creativity. Project description: Can computer be capable of human-level creativity? Is artificial intelligence set to become next the great art/music movement? Artificial intelligence is substantially changing the nature of creative processes. The students will explore the interface between art, music and artificial intelligence. For example, evolutionary image transition can be utilised as inspiration for creating original digital art and videos. The focus will be on developing new tools using machine learning, neural networks and optimisation. The projects can be carried out dependent on the background and interest of the students. We are not able to offer financial assistance.

For more information, please contact: aneta.neumann@adelaide.edu.au.

CSC PhD Applications

The China Scholarship Council (CSC) and the University of Adelaide are jointly offering postgraduate research scholarships to applicants from the People's Republic of China who intend to undertake a Doctor of Philosophy at the University of Adelaide. Please email your expression of interest to hdr_intl_schols@adelaide.edu.au and aneta.neumann@adelaide.edu.au.

Papers accepted at PPSN 2020:

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

Authors: Aneta Neumann and Frank Neumann

Optimising tours for the weighted traveling salesperson problem and the traveling thief problem: A structural comparison of solutions

Authors: Jakob Bossek, Aneta Neumann and Frank Neumann

Evolving Sampling Strategies for One-Shot Optimization Tasks

Authors: Jakob Bossek, Carola Doerr, Pascal Kerschke, Aneta Neumann and Frank Neumann

Papers accepted at GECCO 2020:

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

Authors: Yue Xie, Aneta Neumann, Frank Neumann

Evolving Diverse Sets of Tours for the Travelling Salesperson Problem

Authors: Viet Anh Do, Jakob Bossek, Aneta Neumann, Frank Neumann

Paper accepted at Evolutionary Computation Journal, MIT Press:

Evolutionary Image Transition and Painting Using Random Walks, [download] [arXiv], Mar 2020

Authors: Aneta Neumann, Bradley Alexander, Frank Neumann

Tutorial accepted at GECCO 2020:

Evolutionary Computation for Digital Art,

Authors: Aneta Neumann and Frank Neumann

Papers accepted at ECAI 2020:

Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives, [arXiv] 2020
Authors: Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann and Frank Neumann

Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings,
Authors: Vanja Doskoc, Tobias Friedrich, Andreas Göbel, Aneta Neumann, Frank Neumann and Francesco Quinzan, [arXiv] 2020

Special Session accepted for WCCI 2020 - Theoretical Foundations of Bio-inspired Computation

Organizators: Per Kristian Lehre, Aneta Neumann, Chao Qian

Tutorial accepted at PPSN 2020:

Evolutionary Diversity Optimisation,

Authors: Jakob Bossek, Aneta Neumann, Frank Neumann

Paper accepted at AAAI 2020:AAAI2020

Optimization of Chance-Constrained Submodular Functions,

Authors: B. Doerr, C. Doerr, A. Neumann, F. Neumann, A. M. Sutton

[download], 2020

 
The 2019 Workshop on AI-based Optimisation (AI-OPT 2019)

opArtificial Intelligence based optimisation techniques such as constraint programming, evolutionary computation, heuristic search, mixed integer programming, and swarm intelligence have found many applications in solving highly complex and challenging optimisation problems. Application domains include important areas such as cybersecurity, economics, engineering, renewable energy, health and supply chain management.

 
Tutorial on Evolutionary Computation for Digital Art at GECCO 2019

Art Tutorial GECCO  2019Tutorial on Evolutionary Computation for Digital Art with Frank Neumann accepted at Genetic and Evolutionary Computation Conference (GECCO) 2019. The tutorial slides Evolutionary Computation for Digital Art, Vimeo.

Paper accepted at FOGA 2019, Code:

Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators, Authors: J. Bossek, P. Kerschke, A. Neumann, M. Wagner,  F. Neumann, H. Trautmann

 

STEM WORKSHOPS in German with GOETHE INSTITUT
GI_AN
GI_Aneta_Neumann
 
Papers accepted at GECCO 2019, Code:

Evolutionary Diversity Optimization Using Multi-Objective Indicators - Nominated for Best Paper Award in the track "Genetic Algorithms"

Authors: Aneta Neumann, Wanru Gao, Markus Wagner, Frank Neumann, [download] [researchgate] July 2019

Evolutionary Algorithms for the Chance-Constrained Knapsack Problem

Authors: Yue Xie, Oscar Harper, Hirad Assimi, Aneta Neumann, Frank Neumann, [download] [researchgate] July 2019

Tutorial on Evolutionary Computation for Digital Art at GECCO 2019

Tutorial on Evolutionary Computation for Digital Art with Frank Neumann accepted at Genetic and Evolutionary Computation Conference (GECCO 2019).

GMG Adelaide Forum: Secure and Integrated Energy and Mining Systems, 2019

--Bringing the mining and energy industries together to achieve secure integration across the sector with Prof Stephen Grano, Executive Director of the University of Adelaide’s Institute for Mineral and Energy Resources (IMER).

 

Paper accepted at AAAI 2019:

V. Roostapour, A. Neumann, F. Neumann, T. Friedrich (2019): Pareto optimization for subset selection with dynamic cost constraints. In: Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, download, [arxiv], Jul 2019

Tutorial on Evolutionary Computation for Digital Art at AI 2018

Tutorial on Evolutionary Computation for Digital Art with Frank Neumann accepted at the Australasian Joint Conference on Artificial Intelligence (AI 2018), LNCS 11320, Springer. The tutorial slides Evolutionary Computation for Digital Art, last updated: 11 December 2018.

Aneta has been awarded a Hans-Juergen and Marianna Ohff Research Grant for 2018

This grant will support a research visit at the Algorithm Engineering group lead by Prof. Dr. Tobias Friedrich, the Hasso Plattner Institute Potsdam, Germany. Media: Research grant for Aneta Neumann

Aneta has been awarded an ACM-W scholarship 2018,ACM_W_Awords_Aneta_Neumann

sponsored by Google, Microsoft, Oracle. Media:

Aneta Neumann Recipient of ACM-W Scholarship [09/2018], GECCO 2018, ACM-W Awards

Tutorial on Evolutionary Computation for Digital Art at GECCO 2018,

Tutorial on Evolutionary Computation for Digital Art with Frank Neumann accepted at Genetic and Evolutionary Computation Conference (GECCO) 2018. The tutorial slides Evolutionary Computation for Digital Art, last updated: 20 July 2018.

Big Data Fundamentals MOOC's course

Aneta is a co-designer and co-lecturer for Big Data Fundamentals (Start Date: Mar 1, 2019) course in the Big Data MicroMasters® program, an open online graduate level series of courses https://blogs.adelaide.edu.au/adelaidex/2017/05/24/adelaidex-launches-big-data-micromasters/.

BigData Fundamentals

Cover Page on SIGEVOlution

SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, Volume 10, Issue 3, http://www.sigevolution.org/issues/SIGEVOlution1003.pdf

CS Researcher in SALA Art Exhibition

SALA2018_AnetaNeumannAneta Neumann, a researcher from the School of Computer Science is exhibiting two mixed media artworks in Hub Central. The artworks are inspired by images produced by new methods in evolutionary image transition, pioneered by Aneta in her research toward her PhD. This research carried out within the Optimisation and Logistic Research Group in the School explores the fundamental link between evolutionary processes and generative art. The images will be on display in the Hub until the 18th of August. http://blogs.adelaide.edu.au/cs/2017/08/15/cs-researcher-in-sala-art-exhibition/

Research Interests:

artificial intelligence, machine learning, evolutionary computation, multi-objective optimisation, diversity, generative artcomputational creativity                                                                                                                                             

Publications:

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

Authors: Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann and Frank Neumann

Accepted as a full paper for publication at the ECAI 2020, [arxiv], 2020

ABSTRACT: Real-world optimization problems are often stochastic and dynamic and it is important to tackle stochastic and dynamic environments in a common approach. In this paper, we consider the stochastic chance-constrained knapsack problem where the constraint bound dynamically changes over time. We introduce a Pareto optimization approach for this problem that make use of important tail inequalities such as Chebyshev's inequality and Chernoff bound to estimate the probability of exceeding a given constraint bound. The key part of our approach is the introduction of an additional objective which calculates the minimal constraint bound for which a given solution for the stochastic component would still meet the chance constraint. This objective helps to cater for dynamic changes to the stochastic problem. Our experimental investigations show that the Pareto optimization is highly effective and outperforms its corresponding single-objective approach.

Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings

Authors: Vanja Doskoc, Tobias Friedrich, Andreas Göbel, Aneta Neumann, Frank Neumann and Francesco Quinzan

Accepted as a full paper for publication at the ECAI 2020, [arxiv], 2020

ABSTRACT: We study the problem of maximizing a non-monotone submodular function under multiple knapsack constraints. We propose a simple discrete greedy algorithm to approach this problem, and prove that it yields strong approximation guarantees for functions with bounded curvature. In contrast to other heuristics, this requires no problem relaxation to continuous domains and it maintains a constant-factor approximation guarantee in the problem size. In the case of a single knapsack, our analysis suggests that the standard greedy can be used in non-monotone settings. Additionally, we study this problem in a dynamic setting, in which knapsacks change during the optimization process. We modify our greedy algorithm to avoid a complete restart at each constraint update. This modification retains the approximation guarantees of the static case. We evaluate our results experimentally on a video summarization and sensor placement task. We show that our proposed algorithm competes with the state-of-the-art in static settings. Furthermore, we show that in dynamic settings with tight computational time budget, our modified greedy yields significant improvements over starting the greedy from scratch, in terms of the solution quality achieved.

Optimization of Chance-Constrained Submodular Functions,

Authors: B. Doerr, C. Doerr, A. Neumann, F. Neumann, A. M. Sutton

Accepted as a full paper for publication at the AAAI 2020

[download], 2020

ABSTRACT: Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided.In this paper, we investigate submodular optimization problems with chance constraints. We provide a first analysis on the approximation behavior of popular greedy algorithms for submodular problems with chance constraints. Our results show that these algorithms are highly effective when using surrogate functions that estimate constraint violations based on Chernoff bounds. Furthermore, we investigate the behavior of the algorithms on popular social network problems and show that high quality solutions can still be obtained even ift here are strong restrictions imposed by the chance constraint.

Evolving diverse TSP instances by means of novel and creative mutation operators

Authors: J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann

Accepted as a full paper for publication at the FOGA 2019,

[download], 2019

ABSTRACT: Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.

Quasi-random Agents for Image Transition and Animation

Accepted as for journal publication at Australian Journal of Intelligent Information Processing Systems,

download [arxiv], Dec 2019

Authors: Aneta Neumann, Frank Neumann, Tobias Friedrich

ABSTRACT: Quasi-random walks show similar features as standard random walks, but with much less randomness. We utilize this established model from discrete mathematics and show how agents carrying out quasi-random walks can be used for image transition and animation. The key idea is to generalize the notion of quasi-random walks and let a set of autonomous agents perform quasi-random walks painting an image. Each agent has one particular target image that they paint when following a sequence of directions for their quasi-random walk. The sequence can easily be chosen by an artist and allows them to produce a wide range of different transition patterns and animations.

Evolving Pictures in Image Transition Space

Authors: B. Alexander, D. Hin, A. Neumann, S. Ull-Karim

Accepted as a full paper for publication at the ICONIP 2019

[download], 2019

ABSTRACT: Evolutionary art creates novel images through a processes inspired by natural selection. Images are high dimensional objects, which can present challenges for evolutionary processes. Work to date has handled this problem by evolving compressed or encoded forms of images or by starting with prior images and evolving constrained variations of these. In this work we extend the prior-image concept by evolving interesting images in the transition-space between two bounding images. We define new feature metrics based on proximity to the two bounding images and show how these metrics, combined with other aesthetic features, can be used to drive the creation of new images incorporating features of both starting images. We extend this work further to evolve sets images that are diverse in one and two feature dimensions. Finally, we accelerate this evolutionary process using an autoencoder to capture the transition space and reduce the dimensionality of the search space.

Evolutionary Diversity Optimization Using Multi-Objective Indicators

Authors: Aneta Neumann, Wanru Gao, Markus Wagner, Frank Neumann

Accepted as a full paper for publication at the Genetic and Evolutionary Computation Conference, GECCO 2019, was nominated for Best Paper Award for in the track "Genetic Algorithms"

[download] [paper] [arxiv], 2019

ABSTRACT: Evolutionary diversity optimization aims to compute a diverse set of solutions where all solutions meet a given quality criterion. With this paper, we bridge the areas of evolutionary diversity optimization and evolutionary multi-objective optimization. We show how popular indicators frequently used in the area of multi-objective optimization can be used for evolutionary diversity optimization. Our experimental investigations for evolving diverse sets of TSP instances and images according to various features show that two of the most prominent multi-objective indicators, namely the hypervolume indicator and the inverted generational distance, provide excellent results in terms of visualization and various diversity indicators.

Evolutionary Algorithms for the Chance-Constrained Knapsack Problem

Authors: Yue Xie, Oscar Harper, Hirad Assimi, Aneta Neumann, Frank Neumann

Accepted as a full paper for publication at the Genetic and Evolutionary Computation Conference, GECCO 2019,

[download] [paper] [arxiv], 2019

ABSTRACT: Evolutionary algorithms have been widely used for a range of stochastic optimization problems. In most studies, the goal is to optimize the expected quality of the solution. Motivated by real-world problems where constraint violations have extremely disruptive effects, we consider a variant of the knapsack problem where the profit is maximized under the constraint that the knapsack capacity bound is violated with a small probability of at most $\alpha$. This problem is known as chance-constrained knapsack problem and chance-constrained optimization problems have so far gained little attention in the evolutionary computation literature. We show how to use popular deviation inequalities such as Chebyshev's inequality and Chernoff bounds as part of the solution evaluation when tackling these problems by evolutionary algorithms and compare the effectiveness of our algorithms on a wide range of chance-constrained knapsack instances.

Pareto optimization for subset selection with dynamic cost constraints

Authors: V. Roostapour, A. Neumann, F. Neumann, T. Friedrich

Accepted as a full paper for publication at Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019,

[download] [arxiv], Nov 2018

ABSTRACT: In this paper, we consider subset selection problem for function f with constraint bound B which changes over time. We point out that adaptive variants of greedy approaches commonly used in the area of submodular optimization are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach, we show that this algorithm efficiently computes a $\phi= (\alpha_f/2)(1-\frac{1}{e^{\alpha_f}})$-approximation, where αf is the submodularity ratio of f, for each possible constraint bound b B. Furthermore, we show that POMC is able to adapt its set of solutions quickly in the case that B increases. Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms.

On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem

Accepted as a full paper for publication at Parallel Problem Solving from Nature (PPSN 2018),  [download], BENCHMARK [download],

Authors: Vahid Roostapour, Aneta Neumann, Frank Neumann

ABSTRACT: Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study single- and multi-objective baseline evolutionary algorithms for the classical knapsack problem where the capacity of the knapsack varies over time. We establish different benchmark scenarios where the capacity changes every tau iterations according to a uniform or Normal distribution. Our experimental investigations analyze the behavior of our algorithms in terms of the magnitude of changes determined by parameters of the chosen distribution, the frequency determined by tau and the class of knapsack instance under consideration. Our results show that the multi-objective approaches using a population that caters for dynamic changes have a clear advantage on many benchmarks scenarios when the frequency of changes is not too high.

Discrepancy-based Evolutionary Diversity Optimisation 

Accepted as a full paper for publication at GECCO 2018, [bibtex], [arxiv], [download],

Authors: Aneta Neumann, Wanru Gao, Carola Doerr, Frank Neumann, Markus Wagner

ABSTRACT:  Diversity plays a crucial role in evolutionary computation. While diversity has been mainly used to prevent the population of an evolutionary algorithm from premature convergence, the use of evolutionary algorithms to obtain a diverse set of solutions has gained increasing attention in recent years. Diversity optimization in terms of features on the underlying problem allows to obtain a better understanding of possible solutions to the problem at hand and can be used for algorithm selection when dealing with combinatorial optimization problems such as the Traveling Salesperson Problem. We explore the use of the star-discrepancy measure to guide the diversity optimization process of an evolutionary algorithm.

In our experimental investigations, we consider our discrepancy-based diversity optimization approaches for evolving diverse sets of images as well as instances of the Traveling Salesperson problem where a local search is not able to find near optimal solutions. Our experimental investigations comparing three diversity optimization approaches show that a discrepancy-based diversity optimization approach using a tie-breaking rule based on weighted differences to surrounding feature points provides the best results in terms of the star discrepancy measure.

On the Use of Colour-based Segmentation in  Evolutionary Image Composition

Accepted as a full paper for publication at IEEE CEC 2018 [download],

Authors: Aneta Neumann, Frank Neumann

ABSTRACT: Evolutionary algorithms have been widely used in the area of creativity in order to help create art and music. We consider the recently introduced evolutionary image composition approach based on feature covariance matrices [1] which allows composing two images into a new one based on their feature characteristics. When using evolutionary image composition it is important to obtain a good weighting of interesting regions of the two images. We use colour-based segmentation based on K-Means clustering to come up with such a weighting of the images. Our results show that this preserves the chosen colour regions of the images and leads to composed images that preserve colours better than the previous approach based on saliency masks [1]. Furthermore, we evaluate our composed images in terms of aesthetic feature and show that our approach based on colour-based segmentation leads to higher feature values for most of the investigated features.  

Evolution of Images with Diversity and Constraints Using a Generator Network

Accepted as a full paper for publication at International Conference on Neural Information Processing (ICONIP 2018), [bibtex], [arxiv], [download],

Authors: Aneta Neumann, Christo Pyromallis, Bradley Alexander

ABSTRACT: Evolutionary search has been extensively used to generate artistic images. Raw images have high dimensionality which makes a direct search for an image challenge. In previous work, this problem has been addressed by using compact symbolic encodings or by constraining images with priors. Recent developments in deep learning have enabled a generation of compelling artistic images using generative networks that encode images with lower-dimensional latent spaces. To date, this work has focused on the generation of images concordant with one or more classes and transfer of artistic styles. There is currently no work which uses search in this latent space to generate images scoring high or low aesthetic measures. In this paper, we use evolutionary methods to search for images in two datasets, faces and butterflies and demonstrate the effect of optimising aesthetic feature scores in one or two dimensions. The work gives a preliminary indication of which feature measures promote the most interesting images and how some of these measures interact.

Evolutionary Image Composition Using Feature Covariance Matrices

The Genetic and Evolutionary Computation Conference (GECCO 2017) [ bibtex ] [download] [arxiv],

Authors: Aneta Neumann, Zygmunt L Szpak, Wojciech Chojnacki, Frank Neumann

ABSTRACT: Evolutionary algorithms have recently been used to create a wide range of artistic work. In this paper, we propose a new approach for the composition of new images from existing ones, that retain some salient features of the original images. We introduce evolutionary algorithms that create new images based on a fitness function that incorporates feature covariance matrices associated with different parts of the images. This approach is very flexible in that it can work with a wide range of features and enables targeting specific regions in the images. For the creation of the new images, we propose a population-based evolutionary algorithm with mutation and crossover operators based on random walks. Our experimental results reveal a spectrum of aesthetically pleasing images that can be obtained with the aid of our evolutionary process. 

Evolution of Artistic Image Variants Through Feature Based Diversity Optimisation

The Genetic and Evolutionary Computation Conference (GECCO 2017) [bibtex] [download],

Authors: Bradley Alexander, James Kortman, Aneta Neumann

ABSTRACT: Measures aimed to improve the diversity of images and image features in evolutionary art help to direct search toward more novel and creative parts of the artistic search domain. To date, such measures have focused on relatively indirect means of ensuring diversity in the context of search to maximise an aesthetic or similarity metric. In recent work on TSP problem instance classification, selection based on a direct measure of each individual's contribution to diversity was successfully used to generate hard and easy TSP instances. In this work, we use an analogous search framework to evolve diverse variants of a source image in one and two feature dimensions. The resulting images show the spectrum of effects from transforming images to score across the range of each feature. The evolutionary process also reveals interesting correlations between feature values in both one and two dimensions.

Multi-objectiveness in the Single-objective Traveling Thief Problem

The Genetic and Evolutionary Computation Conference (GECCO 2017), poster, [bibtex] [download],

Authors: Mohamed El Yafrani, Shelvin Chand, Aneta Neumann, Belaid Ahiod, Markus Wagner

ABSTRACT: Multi-component problems are optimization problems that are composed of multiple interacting sub-problems. The motivation of this work is to investigate whether it can be better to consider multiple objectives when dealing with multiple interdependent components. Therefore, the Travelling Thief Problem, a relatively new benchmark problem, is investigated as a bi-objective problem. An NSGA-II adaptation for the bi-objective model is compared to three of the best known algorithms for the original single-objective problem. The results show that our approach generates diverse sets of solutions while being competitive with the state-of-the-art single-objective algorithms.

Evolutionary Image Transition Using Random Walks 

Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART’17). Springer, Cham. [bibtex] [download], [download Springer]

Authors: Aneta Neumann, Bradley Alexander, Frank Neumann

ABSTRACT: We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random walks and study how their combination with a baseline mutation operator can lead to interesting image transition processes in terms of visual effects and artistic features. Using feature-based analysis we investigate the evolutionary image transition behaviour with respect to different features and evaluate the images constructed during the image transition process.        

A Modified Indicator-based Evolutionary Algorithm (mIBEA)

IEEE Congress on Evolutionary Computation 2017, San Sebastián, [bibtex] [download],

Authors: Li,W, Ozcan, E, John, R, Drake, JH, Neumann, A, Wagner, M

ABSTRACT: Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the search process towards a good set of trade-off solutions. One commonly used approach of this nature is the indicator-based evolutionary algorithm (IBEA). In this study, we highlight the solution distribution issues within IBEA and propose a modification of the original approach by embedding an additional Pareto-dominance based component for selection. The improved performance of the proposed modified IBEA (mIBEA) is empirically demonstrated on the well-known DTLZ set of benchmark functions. Our results show that mIBEA achieves comparable or better hypervolume indicator values and epsilon approximation values in the vast majority of our cases (13 out of 14 under the same default settings) on DTLZ1-7. The modification also results in an over 8-fold speed-up for larger populations.

The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation

International Conference on Neural Information Processing (ICONIP2016) [bibtex] [url] [download],

Authors: Aneta Neumann, Bradley Alexander, Frank Neumann

ABSTRACT: Evolutionary algorithms have been used in many ways to generate digital art. We study how the evolutionary processes can be used for evolutionary art and present a new approach to the transition of images. Our main idea is to define evolutionary processes for digital image transition, combining different variants of mutation and evolutionary mechanisms. We introduce box and strip mutation operators which are specifically designed for image transition. Our experimental results show that the process of an evolutionary algorithm in combination with these mutation operators can be used as a valuable way to produce unique generative art.

Evolutionary Image Transition Based on Theoretical Insights of Random Processes [download]

Authors: Aneta Neumann, Bradley Alexander, Frank Neumann

ABSTRACT: Evolutionary algorithms have been widely studied from a theoretical perspective. In particular, the area of runtime analysis has contributed significantly to a theoretical understanding and provided insights into the working behaviour of these algorithms. We study how these insights into evolutionary processes can be used for evolutionary art. We introduce the notion of evolutionary image transition which transfers a given starting image into a target image through an evolutionary process. Combining standard mutation effects known from the optimization of the classical benchmark function OneMax and different variants of random walks, we present ways of performing evolutionary image transition with different artistic effects.

Invited lectures/talks/scientific visit:
Conference Programme Committee/Member:
  • Association for the Advancement of Artificial Intelligence (AAAI), 2019-2020
  • The International Conference on Machine Learning (ICML), 2020
  • The International Joint Conference on Artificial Intelligence (IJCAI), 2020
  • The Genetic and Evolutionary Computation Conference  (GECCO), 2020
  • The International Conference on Parallel Problem Solving from Nature (PPSN), 2020
  • The European Conference on Artificial Intelligence (ECAI), 2020
  • The Evolutionary Computation Journal (ECJ), since 2017
  • Australasian Conference on Artificial Life and Computational Intelligence, 2016-2019
  • Association for Computing Machinery Membership (ACM), 2018-2019
  • IEEE Computational Intelligence Society Membership, 2017-2019
  • IEEE Theoretical Foundations of Bio-inspired Computation Task Force, 2017-2019
  • Association for Computing Machinery Membership, SIGEVO 2017-2019
  • ECMS Volunteer & Ambassador Program, 2016-2019
Presentations and exhibitions:
  • SALA, South Australia Living Artists Festival, August 2016-2018
  • Media Article, CS Researcher in SALA Art Exhibition, the University of Adelaide

 

 

 

 

 

 

 

 

 

 

SALA2018_AnetaNeumann

 

 

 

If you have any questions about my work or suggestions for this webpage, please send me an email.

Entry last updated: 1 June 2020

 

2018 Hans-Juergen and Marianna Ohff Research Grant,

2018 ACM-W scholarship sponsored by Google, Microsoft, Oracle,

2018 ACM Travel Grant,

2017 ACM Travel Grant,

2017 EvoStar Travel Bursaries Award,

2016 School of Computer Science Postgraduate Scholarship, University of Adelaide, Australia

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  • 2018, Lecturer, Mining Big Data,  3-year and master students of Computer Science, Sem 1, 3 Units
  • 2017, EdX MOOC, co-designer and co-lecturer Big Data Fundamentals, MicroMasters 
  • 2017, Lecturer, Foundations of Computer Science, Master of Computing and Innovation, Sem 1, 6 Units
  • 2017, University of Adelaide, School of Computer Science,  Australia, Areas: Supervisor: Introduction to Programming Processing, EdX Course: Think. Create. Code, Sem 1
  • 2016, University of Adelaide, School of Computer Science,  Australia, Areas: Supervisor: Introduction to Programming Processing, EdX Course: Think. Create. Code, Sem 2
  • 2012, University of Adelaide, School of Computer Science,  Australia, Areas: Supervisor: Object-oriented programming in Java
  • 2012, University of Adelaide, School of Computer Science,  Australia, Areas: Supervisor: Internet Computing
  • 2011, University of Adelaide, School of Computer Science,  Australia, Areas: Supervisor: Object-oriented programming in Java
  • 2011, University of Adelaide, School of Computer Science,  Australia, Areas: Supervisor: Introduction to programming for engineers (Matlab/C) 
  • 2008, University of Applied Sciences, Saarbruecken, Germany, Areas: Lecturer for Software Technology and Programming in Java 
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  • Current Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2020 Co-Supervisor Many Objective Evolutionary Diversity Optimization Doctor of Philosophy Doctorate Full Time Mr Adel Nikfarjam
    2019 Co-Supervisor Unlocking Complex Resources through Lean Processing Doctor of Philosophy Doctorate Full Time Miss Yue Xie
  • Other Supervision Activities

    Date Role Research Topic Location Program Supervision Type Student Load Student Name
    2017 - 2017 Co-Supervisor Evolution of Images with Diversity and Constraints Using Deep-learned Surrogate Functions Department of Computer Science Other Christo Pyromallis
    2017 - ongoing Co-Supervisor Using Deep Learning to Discover Image Maximising Aesthetic Features Department of Computer Science Other Ryan Matulick
    2017 - 2018 Co-Supervisor Evolution of Images with Diversity and Constraints using a Generator Network Department of Computer Science Computer Science Student and Winner of ECMS Summer Research Scholarship 2017 Other Christo Pyromallis
    2016 - 2017 Co-Supervisor Evolution of artistic image variants through feature based diversity optimisation Department of Computer Science Computer Science Student and Winner of ECMS Summer Research Scholarship 2016 Other James Kortman
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  • Committee Memberships

    Date Role Committee Institution Country
    2018 - ongoing Member Australasian Joint Conference on Artificial Intelligence AI 2018 Australia
    2018 - ongoing Member IEEE Congress on Evolutionary Computation
    2016 - ongoing Member Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2017)
  • Memberships

    Date Role Membership Country
    2018 - ongoing Member Association for Computing Machinery (ACM) United States
    2017 - ongoing ACM SIGEVO United States
    2017 - ongoing IEEE Theoretical Foundations of Bio-inspired Computation Task Force United Kingdom
    2017 - ongoing IEEE Membership United States
    2017 - ongoing IEEE Computational Intelligence Society United States
    2017 - ongoing IEEE Women in Engineering United States
    2016 - ongoing Australian Network for Art and Technology Australia
    2016 - ongoing ASIA PACIFIC NEURAL NETWORK SOCIETY (APNNS) Japan
    2015 - ongoing University Theatre Guild Australia
  • Position: Researcher
  • Phone: 83134519
  • Email: aneta.neumann@adelaide.edu.au
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor 4
  • Room: 4 53
  • Org Unit: Faculty of Engineering Computer & Math Sciences

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