Mrs Aneta Neumann

Aneta Neumann
Higher Degree by Research Candidate
PhD Candidate
School of Computer Science
Faculty of Engineering, Computer and Mathematical Sciences

Aneta graduated from the University of Kiel, Germany (Dipl.Inf) in Computer Science and is currently undertaking postgraduate research at the School of Computer Science, The University of Adelaide. Her main research interest is understanding the fundamental link between Evolutionary Algorithms and Generative Art.

Supervisors: Dr. Bradley Alexander and Prof. Zbigniew Michalewicz

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Mrs Aneta Neumann

Aneta graduated from the University of Kiel, Germany (Dipl.Inf) in Computer Science and is currently undertaking postgraduate research at the School of Computer Science, The University of Adelaide. Her main research interest is understanding the fundamental link between Evolutionary Algorithms and Generative Art.

Supervisors: Dr. Bradley Alexander and Prof. Zbigniew Michalewicz

Evolutionary Image TransitionEITResearch Interests:EIT

generative art, multi-objective optimisation, diversity   

evolutionary computation, aesthetic measure, features

                                                                                                                                                             

             Ingenuity 2016 Exhibition, Adelaide Convention Centre

Poster Presentation - Ingenuity 2016 - Faculty of Engineering, Computer and Mathematical Science 

Publications:

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 

Proc. 6th Int. Conf. Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART’17). Springer, Cham. [bibtex] [download]

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, 2017

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

The 23rd 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:
  • The scientific visit at Algorithm Engineering Group at the Hasso Plattner Institute, Potsdam, Germany Apr/Mai 2017
  • University of Nottingham, Nov 2016
  • University of Sheffield, Dec 2016
  • Goldsmiths, University of London, Dec  2016
  • University College, London, Dec 2016
Conference Programme Committee/Student Member:
  • Association for Computing Machinery Membership, 2017
  • IEEE Computational Intelligence Society Membership, 2017
  • IEEE Theoretical Foundations of Bio-inspired Computation Task Force, 2017
  • Australasian Conference On Artificial Life and  Computational Intelligence, 2016
  • ECMS Volunteer & Ambassador Program, 2016, 2017
Presentations and exhibitions:
  • SALA, South Australia Living Artists Festival, August, 2016

 

 

 

 

 

 

 

 

 

 

 

 

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

Entry last updated: 14 August 2017

 

Appointments

Date Position Institution name
2016 PhD Candidate University of Adelaide

Language Competencies

Language Competency
English Can peer review
German Can peer review
Polish Can peer review

Education

Date Institution name Country Title
2007 Kiel University Germany Diplom in Computer Science (Dipl.Inf.)
Technical University of Dortmund Germany Vordiplom in Computer Science

Postgraduate Training

Date Title Institution Country
Introduction to University Teaching University of Adelaide Australia

Research Interests

Artificial Intelligence, Creative Arts, Film, Television and Digital Media, Neural, Evolutionary and Fuzzy Computation, Optimisation

Conference Papers

Year Citation
2017 Neumann, A., Szpak, Z. L., Chojnacki, W. & Neumann, F. (2017). Evolutionary Image Composition Using Feature Covariance Matrices. The Genetic and Evolutionary Computation Conference (GECCO 2017). Germany.
2017 Alexander, B., Kortman, J. & Neumann, A. (2017). Evolution of Artistic Image Variants Through Feature Based Diversity Optimisation. The Genetic and Evolutionary Computation Conference (GECCO 2017). Berlin.
2017 Li, W., Ozcan, E., John, R., Drake, J., Neumann, A. & Wagner, M. (2017). A Modified Indicator-based Evolutionary Algorithm (mIBEA). IEEE Congress on Evolutionary Computation 2017. San Sebastián.
2017 Neumann, A., Alexander, B. & Neumann, F. (2017). Evolutionary Image Transition Using Random Walks. Proc. 6th Int. Conf. Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART’17). Amsterdam. 10.1007/978-3-319-55750-2_16
2016 Neumann, A., Alexander, B. & Neumann, F. (2016). The evolutionary process of image transition in conjunction with box and strip mutation. The 23rd International Conference on Neural Information Processing (ICONIP 2016). Kyoto, Japan. 10.1007/978-3-319-46675-0_29
Neumann, A., Szpak, Z. L., Chojnacki, W. & Neumann, F. (). Evolutionary Image Composition Using Feature Covariance Matrices.

Curated or Produced Public Exhibition or Events

Year Citation
2016 Neumann, A.; (2016); ART EXHIBITION SALA 2016, THE UNIVERSITY OF ADELAIDE STUDENT ART EXHIBITION, the South Australian Living Arts Festival 2016 (SALA2016); ADELAIDE;

Working Paper

Year Citation
2016 Neumann, A., Alexander, B. & Neumann, F.; (2016); The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation;
2016 Neumann, A., Alexander, B. & Neumann, F.; (2016); Evolutionary Image Transition Based on Theoretical Insights of Random Processes.;

2017 ACM Travel Grant,

2017 EvoStar Travel Bursaries Award,

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

  • 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 

2016-2017, James Kortman, Computer Science Student and Winner of ECMS Summer Research Scholarship 2016

2017, Ryan Matulick, Computer Science Student, Undergraduate Project

2017, Christo Pyromallis, Computer Science Student, Undergraduate Project

Memberships

Date Role Membership Country
2017 - ongoing IEEE Brain 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 Association for Computing Machinery 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

Committee Memberships

Date Role Committee Institution Country
2016 - ongoing Member Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2017)
Position
PhD Candidate
Phone
83134519
Campus
North Terrace
Building
Ingkarni Wardli
Room Number
4 53
Org Unit
Faculty of Engineering Computer & Math Sciences

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