Mrs Aneta Neumann

Aneta Neumann
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

Connect With Me

External Profiles

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

Research Interests:
  • generative art
  • evolutionary computation
  • multi-objective optimisation
  • aesthetic measure
  • features, diversity

                       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) To appear View PDF

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 im- ages. 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 aes- thetically 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) To appear

Authors: Bradley Alexander, James Kortman, Aneta Neumann

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.

Evolutionary Image Transition Using Random Walks 

Proc. 6th Int. Conf. Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART’17). Springer, Cham. To appear

Authors: Aneta Neumann, Bradley Alexander, Frank Neumann

ABSTRACT: We present a study demonstrating how random walk algo- rithms can be used for evolutionary image transition. We design differ- ent 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 evolu- tionary image transition behaviour with respect to different features and evaluate the images constructed during the image transition process.        

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

The 23rd International Conference on Neural Information Processing (ICONIP2016) View PDF 

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 View PDF 

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:
  • 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:
  • Australasian Conference On Artificial Life and  Computational Intelligence, 2016
  • IEEE Theoretical Foundations of Bio-inspired Computation Task Force, 2017
Presentations and exhibitions:
  • SALA, South Australia Living Artists Festival, August, 2016

 

 

 

 

 

 

 

 

 

 

 

 

Entry last updated: 29 March 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
Kiel University Germany Diplom in Computer Science (Dipl.Inf.)
Technical University of Dortmund Germany Vordiplom in Computer Science

Keywords

Artificial Intelligence & Image Processing, Neural, Evolutionary and Fuzzy Computation, Optimisation, Performance and Installation Art

Conference Papers

Date Citation
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 (ICONIP2016), Kyoto, Japan 10.1007/978-3-319-46675-0_29

Curated or Produced Public Exhibition or Events

Date 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

Date 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 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 

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

Memberships

Date Role Membership Country
2017 IEEE Theoretical Foundations of Bio-inspired Computation Task Force United Kingdom
2017 Asia Pacific Neural Network Society (APNNS)
2016 Australian Network for Art and Technology Australia
2016 ASIA PACIFIC NEURAL NETWORK SOCIETY (APNNS) Japan
2016 Member University Theatre Guild Australia
2015 University Theatre Guild Australia

Committee Memberships

Date Role Committee Institution Country
2016 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

top