Director
Geoff Nitschke (PhD VU Amsterdam)
Email : gnitschke@cs.uct.ac.za
Research interests : Artificial Life, Evolutionary Machine Learning, Neuro-Evolution, Evolutionary Robotics, Swarm Intelligence and Robotics.
Research profile : Geoff Nitschke is head of the Evolutionary Machine Learning group and Associate Professor at the Department of Computer Science, School of Information Technology, University of Cape Town. He has been working in research on biologically inspired computational intelligence for over 15 years with over 100 publications in peer-reviewed journals, conference proceedings and edited volumes. This research has been supported by grants totaling approximately 20 million ZAR from national and international funding agencies. Geoff is a strong proponent of evolutionary computation and robotics, consistently presenting at key conferences such as Artificial Life (ALIFE), Genetic and Evolutionary Computation Conference (GECCO), the IEEE Congress on Evolutionary Computation (IEEE CEC) and the IEEE Symposium series on Computational Intelligence (IEEE SSCI). Geoff is also associate editor of the Adaptive Behavior and Frontiers in Robotics and AI journals, and participates in numerous international research visits. Post-doctoral research includes a National Research Foundation (NRF) funded position at the Computational Intelligence Research Group, University of Pretoria, South Africa (2009-2011) and a Japan Society for the Promotion of Science (JSPS) funded position at Ikegami Lab, University of Tokyo, Japan (2011-2012). Sabbatical and research visits include invitations to the Computational Intelligence Group, VU Amsterdam, Netherlands (2017), Earth Life Sciences Institute, Tokyo, Japan (2017-2018), Robotics and Autonomous Systems Group, CSIRO, Brisbane, Australia (2019-2020), School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom (2022), Department of Information Sciences, Ochanomizu University, Tokyo, Japan (2023), and School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, Australia (2023).
Postdoctoral
Sabre Didi (PhD UCT)
Email : DDXSAB001@myuct.ac.za
Research topic : Automated Strategic Defence of Computer Networks
Abstract : Globalization has brought increasing dependency on large interconnected networks for the resource (data) control, such as the Internet, communications networks, and power grids. However, such inter-connectivity, coupled with the lack of global view of what is happening in these networks, can lead to tremendous problems in network reliability. For example, small local failures easily propagates to entire networks, causing loss of service and data corruption. Also, deliberate attacks, such as denial of service attacks, viruses and other malware, can easily cause widespread havoc. This research investigates the efficacy of automated software agents (self-adapting computer network-based programs) that effectively traverse networks to protect as many resources as possible from failure and malicious attacks. The goal of such automated agents is to maximally restrict the number of resources damaged (due, for example, to intentional malicious attacks or unintentional network hub failures). To address this, we will create a ”resource protection” agent-based system that capture the essential aspects of this problem. That is, a realistic and sophisticated computer network simulation, where defender agents are tasked with protecting network resources before they are damaged by an intentional (or unintentional) ”adversary” agents (for example, viruses, malware or denial of service attacks).
Doctoral
Bingle Kruger (B.Eng Stellenbosch, MPhil UCT)
Email : KRGBIN001@myuct.ac.za
Thesis topic : Automated Strategic Defence of Computer Networks
Abstract : Globalisation has brought increasing dependency on large interconnected networks for the resource (data) control, such as the Internet, communications networks, and power grids. However, such inter-connectivity, coupled with the lack of global view of what is happening in these networks, can lead to tremendous problems in network reliability. For example, small local failures easily propagates to entire networks, causing loss of service and data corruption. Also, deliberate attacks, such as denial of service attacks, viruses and other malware, can easily cause widespread havoc. This research topic proposes investigating the efficacy of automated software agents (self-adapting computer network-based programs) that effectively traverse networks to protect as many resources as possible from failure and malicious attacks. The goal of such automated agents is to maximally restrict the number of resources damaged (due, for example, to intentional malicious attacks or unintentional network hub failures). To address this, we will create a ”resource protection” agent-based system that capture the essential aspects of this problem. That is, a realistic and sophisticated computer network simulation, where defender agents are tasked with protecting network resources before they are damaged by adversarial agents (for example, viruses, malware or denial of service attacks).
Innocent Sibanda (BTech Hons HIT, MSc Hertfordshire)
Email : SBNINN003@myuct.ac.za
Thesis topic : Evolutionary Dynamics of Asexual Populations at High Mutation Rates
Abstract : Evolutionary dynamics of asexual populations at high mutation rates have long been a central topic of interest to biologists and evolutionary theorists. Biological functions can be robust to changes in sequence; therefore, many different sequences can perform the same function, and when connected with point mutations, these sequences can form a neutral network that allows organisms to explore sequence space without losing function. Neutral network concept is critical in evolutionary biology because it represents a source of genetic variation that can accumulate and spread through a population without significantly impacting fitness. This research explores how neutral network’s topology influences population distribution and mutational robustness in asexual populations at high mutation rates; in addition, this work will investigate possible trade-offs between population distribution and robustness influenced by neutral network topology and, lastly, the effects of interactions between higher-order epistasis and neutral network topology on population distribution and robustness. Datasets with a good fitness landscape will be sourced from the National Institutes of Health database. Combination of artificially generated network science models and data driven experimental approach will be used to carry out this research.
Thabo Ntsoko (MSc.Eng UCT)
Email : NTSTHA019@myuct.ac.za
Thesis topic : Evolutionary Collective Behaviour Transfer
Abstract : This thesis investigates various evolutionary search methods coupled with collective behaviour (multi-agent policy) transfer as a means to more effectively and efficiently solve disparate ranges of collective behaviour (multi-agent) tasks across related problem domains. Specifically, the research investigates the interaction of various behaviour (controller) representations, behaviour evolution and policy transfers methods, on facilitating the effective transfer of evolving robotic controller across various collective robotic tasks -- for example, collective behaviours evolved for a robot group to solve a cooperative reconnaissance task could be transferred (with further online adaptation) to another robot group tasked with cooperative object gathering and transport, and eventually robot behaviour adapted in this task would be transferable to solve more complex collective behaviour tasks.
Master's
Farzana Haque Toma (BSc Hons BracU)
Email : TMXFAR001@myuct.ac.za
Thesis topic : Navigating Complex Design problems with Multi-Objective Evolutionary Algorithms
Abstract : Complex design challenges are often characterized by conflicting objectives, requiring robust and versatile optimisation techniques. These problems are common in fields such as engineering, urban planning, software development, robotics, drug design, and energy systems where solutions must balance trade-offs between competing criteria. The advent of Multi-Objective Evolutionary Algorithms (MOEAs) has contributed to tackling complex design problems. MOEAs take inspiration from biological evolution and are used in optimising multiple, often conflicting objectives. Having conflicting objectives is a common scenario in sophisticated design challenges. Examples of complex multi-objective design problems include simulated chemical product design and architectural building design. Both are multi-objective design problems, where optimal solutions are a trade-off between minimisation and maximisation objectives such as material cost and type. This research aims to provide an MOEA framework for diverse design problems, using simulated molecular property optimisation and building component optimisation as case studies to demonstrate MOEA applicability and effectiveness for general product design.
Bilal Aslan (BSc Hons UCT)
Email : ASLBIL001@myuct.ac.za
Thesis topic : Automated Product Design
Abstract : Currently consumer products ranging from plastics, to cosmetics to batteries are manufactured from complex compositions of chemical elements and pre-designed chemical substances. An ongoing challenge in the design of new products is to: 1) minimise manufacturing and material costs, 2) minimise the expected environmental impact of the product (during manufacturing and after consumer use), and to 3) only use specific (regulated) materials and chemical compositions. Thus, novel product design can be formulated as a multi-objective optimisation problem. Specifically, where the design of any new product simultaneously satisfies all these constraints, but the product designer is able to manage the weighting (relative importance) of each design objective (constraint). This enables the automated production of a broad array of new products that satisfy the design objectives to varying degrees. A product designer would then ideally select one of several automatically designed products according their own specific constraints for how expensive a product can be, what materials can be used in its manufacture, and what the extent of its expected environmental impact can be. This research investigates multi-objective evolutionary algorithms to automate the design of a vast array of products, given a pre-defined set of materials and chemical substances usable in the design and manufacturing processes, and metrics for expected economic and environmental cost.
Rhett Flanagan (BSc Hons UCT)
Email : FLNRHE001@myuct.ac.za
Thesis topic : Evolving Modularity and Folding in Self-Designing Collective Robotic Systems
Abstract : Self-designing collective robotics systems are a topic that shows potential for tasks in areas that are difficult for humans to explore and develop in, such as in the deep ocean or space. This concept is, however, difficult to implement in real-world applications due to resource constraints. This research investigates the viability of using self-designing systems using evolutionary ideas while considering such constrained development resources. It approaches the conservation of resources through modular design and the inclusion of origami-inspired folding techniques. Modular design allows for better recyclability, as working modules can be scavenged from failed units for reuse, and modules can be relocated and repurposed for other tasks in future. Folding allows these systems to use less material to create flexible and durable structures while simultaneously allowing for better conservation of space and weight.
Jeremy Breytenbach (B.Eng Pretoria)
Email : BRYJER002@myuct.ac.za
Thesis topic : Behavioral Diversity for Policy Transfer in Robot Teams: RoboCup Case Study
Abstract : This thesis evaluates various evolutionary search methods to direct neural controller evolution in company with policy (behaviour) transfer across increasingly complex collective robotic (RoboCup keep-away) tasks. Robot behaviors are first evolved in a source task and then transferred for further evolution to more complex target tasks. The efficacy of various behavioural diversity (non-objective) evolutionary search methods for collective behaviour evolution and transfer across increasingly complex keep-away tasks are evaluated in comparison to objective (fitness function) based evolutionary search methods. Collective behaviour effectiveness is measured as the average task performance of transferred and evolved behaviors, where task performance is the average time the ball is controlled by a keeper team.
Alumni
Brandon Gower-Winter (BSc Hons UCT, MSc UCT)
Email : GWRBRA001@myuct.ac.za
MSc Thesis : Self-Adapting Simulated Artificial Societies.[PDF].
Current : PhD Candidate, Information and Computing Sciences, Utrecht University, Netherlands.
Sindiso Mkhatshwa (B.Eng UCT, MSc UCT)
Email : MKHSIN035@myuct.ac.za
MSc Thesis : Body and Brain Quality-Diversity in Robot Swarms.[PDF].
Current : Research Assistant, Computer Science Department, University of Cape Town, South Africa.
Scott Hallauer (BSc Hons UCT, MSc UCT)
Email : HLLSCO001@myuct.ac.za
MSc Thesis : The Impact of Behavioural Diversity in the Evolution of Multi-Agent Systems Robust to Dynamic Environments.[PDF].
Current : Software Engineer, Amazon Web Services, Cape Town, South Africa.
Leon Coetzee (B.Bdg.A Port Elizabeth, MSc UCT)
Email : leon.coetzee@uct.ac.za
MSc Thesis : Evolution of Sun-Shades Outside Building Facades.[PDF].
Current : CIT Liaison Officer, School of Architecture, Planning and Geomatics, UCT, Cape Town, South Africa.
Sasha Abramowitz (BSc Hons UCT, MSc UCT)
Email : ABRSAS002@myuct.ac.za
MSc Thesis : Scalable Hierarchical Evolution Strategies.[PDF].
Current : Software Engineer, InstaDeep Ltd, Cape Town, South Africa.
David Jones (BSc Hons UCT, MSc UCT)
Email : JNSDAV026@myuct.ac.za
MSc Thesis : Gaining Perspective with an Evolutionary Cognitive Architecture for Intelligent Agents.[PDF].
Current : Software Engineer, Maholla, Cape Town, South Africa.
Rob Maccallum (BSc Hons Rhodes, MSc UCT)
Email : MCCROB015@myuct.ac.za
MSc Thesis : Automatic Hit-to-Lead Optimization: Optimizing ligand binding affinity through the application of deep Q-learning to docking simulations.[PDF].
Current : Software Engineer, Zulzi, Cape Town, South Africa.
Celia Pienaar (LLM UNISA, LLB Stellenbosch, MIT UCT)
Email : PNRCEL002@myuct.ac.za
MSc Thesis : Machine Learning in Predictive Analytics on Judicial Decision-Making.[PDF].
Current : Legal Services Improvement Manager, Bowmans, Johannesburg, South Africa.
Matthew Cherry (BSc Hons UCT, MSc UCT)
Email : CHRMAT011@myuct.ac.za
MSc Thesis : Distributed Autonomous Intersection Management with Neuro-evolution.[PDF].
Current : Software Engineer, Mystic AI, London, UK.
Tassallah Amina Abdullahi (BSc Hons UCT, MSc UCT)
Email : ABDTAS008@myuct.ac.za
MSc Thesis : Predicting Diarrhoea Outbreak with Climate Change.[PDF].
Current : PhD Student in Machine Learning, Brown University, Providence, Rhode Island, USA.
Martin Ombura (BCom Hons UCT, MSc UCT)
Email : OMBMAR001@myuct.ac.za
MSc Thesis : The Performance of Coevolutionary Topologies in Developing Competitive Tree Manipulation Strategies for Symbolic Regression. [PDF].
Current : Software Engineer, GoDaddy, Cape Town, South Africa.
Allen Huang (BSc Hons UCT, MSc UCT)
Email : HNGALL001@myuct.ac.za
MSc Thesis : Neuro-Evolution Search Methodologies for Collective Self-Driving Vehicles. [PDF].
Current : Head Of Business Intelligence, Sameday Health, Los Angeles, California, USA.
Sabre Didi (MSc NUST Zimbabwe, PhD UCT)
Email : DDXSAB001@myuct.ac.za
PhD Thesis : Neuro-Evolution Behavior Transfer for Collective Behavior Tasks. [PDF].
Current : Postdoctoral Fellow, University of Cape Town, South Africa.
Edmore Moyo (BSc Hons NUST Zimbabwe, MSc UCT)
Email : MYXEDM001@myuct.ac.za
MSc Thesis : Accelerated Cooperative Co-Evolution on Multi-core Architectures. [PDF].
Current : Software Engineer, Allan Gray Proprietary Limited, Cape Town, South Africa.
Ryan Goss (MTech NMMU, PhD UCT)
Email : GSSRYA001@myuct.ac.za
PhD Thesis : APIC: A Method for Automated Pattern Identification and Classification. [PDF].
Current : Technical Director, Rubrik, Inc., Palo Alto, USA.
David Shorten (BSc Hons UCT, MSc UCT, PhD USYD)
Email : SHRDAV015@myuct.ac.za
MSc Thesis : Spectral Analysis of Neutral Evolution. [PDF].
Current : Postdoctoral Researcher, School of Mathematical Sciences, Faculty of Engineering, Computer and Mathematical Sciences, University of Adelaide, Australia.
Sunrise Wang (BSc Hons UP, MSc UCT, PhD UGA)
Email : WNGSUN001@myuct.ac.za
MSc Thesis : Evolving Controllable Emergent Crowd Behaviours with Neuro-Evolution. [PDF].
Current : Postdoctoral Researcher, Laboratoire J.-L. Lagrange, L’Observatoire de la Côte d’Azur, Nice, France.