Time complexity Analysis of Bio-Inspired Computation -
Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China
Introduction
Applications are invited for a fully-funded Research Fellow in the time complexity analysis of bio-inspired computation techniques such as evolutionary algorithms, genetic algorithms, artificial immune systems which are widely used heuristic search techniques at the heart of artificial intelligence.
About the project
Bio-inspired meta-heuristics are general-purpose optimization paradigms that draw inspiration from biological systems Popular examples include evolutionary algorithms, genetic algorithms and artificial immune systems. The AI-Theory Lab works towards providing a theoretical foundation for understanding the working principles of these heuristic algorithms by quantifying how quickly they find satisfactory solutions for various problems, thus explaining when and why they are efficient. This understanding exposes how performance depends on algorithmic parameters, enables informed choices as to when to use what kind of heuristic and allows the design of better bio-inspired algorithms.
The aim of the project is to develop the mathematical methodology for explaining and predicting the performance of bio-inspired search heuristics. The methodology will be used to derive and extend the theoretical foundations of bio-inspired computation.
Selected topics include the performance analysis of:
a) Population-based search heuristics: highlighting their advantages over single-trajectory algorithms and/or the advantages of recombination over mutation-only algorithms
b) Algorithm configurators: how to evolve the optimal parameter settings for the meta-heuristic
c) Hyper-heuristics: how to evolve the meta-heuristic itself
d) Genetic programming: how to evolve computer programs effectively;
Person Specification
- PhD in computer science (or close to completion) or closely related area
- Expertise in some or all of the following:
- Theory of bio-inspired computation
- Algorithm time complexity analysis and computational complexity
- Computational complexity analysis of randomized algorithms
- Analysis of stochastic processes
- Excellent computer programming skills (JAVA, C)
- Publication record commensurate with career stage in high impact journals and conference proceedings
- Experience of Latex, SVN, GIT or analogue
Main Duties and Responsibilities
- Contribute to the development of mathematical techniques for the time complexity of bio-inspired optimization heuristics
- Perform runtime analyses of bio-inspired search heuristics for combinatorial optimisation problems
- Investigate the impact of algorithmic parameters on the overall performance and the impact of automatic adaptation of the parameters
- Carry out computational experiments required for the achievement of the research goals
- Plan work activities to ensure deliverables and deadlines are met while continuously monitoring progress
- Disseminate the results via project meetings, conference papers, conference presentations and journals of the highest quality as well as impact delivery activities (special session and tutorial organization at conferences
- Collaborate closely with research collaborators world-wide
- Undertake activities to increase own leadership and professional standing in the community and international scale
- Contribute to the intellectual growth of the research group by co-supervising research students
About the University and Department
Established in 2010 with the mission to reform Chinese tertiary education and become a top-notch international research university, SUSTech was launched in the tech capital city of Shenzhen. SUSTech is becoming the important epicentre for China’s science and technology academic research and for the cultivation of innovative minds. The rapid ascent of SUSTech onto the global stage is remarkable. In the Times Higher Education (THE) World university Rankings 2023, it ranked 8th in Mainland China and 166th among the universities in the world. In THE Young Universities Rankings 2024, SUSTech was ranked 1st in China.
The SUSTech campus sits in the rolling hills of Nanshan District, with the verdant green lawns reflecting the environmentally friendly policies of the university. The natural and tranquil environment combines perfectly with the modern style of Shenzhen and its convenient location. With the campus covering an area of nearly 2 square kilometers, there is plenty of room for students to cogitate and consider their research or relax and enjoy their lives on campus. With students transiting the campus on foot, by bike or utilizing our convenient electric shuttle buses, its commitment to environmental sustainability is strong.
Located in the dynamic metropolis of Shenzhen, China’s Silicon Valley, SUSTech is centered on a thriving ecosystem of entrepreneurship, innovation and research. Some 43 per cent of the total PCT patent applications in China came from Shenzhen in 2017, and the city shows no signs of slowing down. As China’s research and development center, it is the perfect place for entrepreneurs, researchers and innovators alike to make their home alongside tech giants such as Huawei, Tencent, BYD, DJI, BJI and Mindray.
Shenzhen is also only distant 17 minutes from Hong Kong city centre by high speed train and about an hour from Macau by ferry.
The successful candidate will join the recently established AI-Theory Lab in the department of Computer Science and Engineering with world-leading expertise in bio-inspired computation.
Salary
332,550-450,000 RMB per annum for 2 years.
Meal supplement and festival expenses allowances as well as high/low temperature subsidies are also provided. Funding is available for conference attendance and collaborative research visits to related research groups in organizations world-wide. The AI-Theory Lab at SUSTech maintains effective collaborations with all the research organizations with major expertise in the theory of bio-inspired computation world-wide.
Line Manager
Professor Pietro S. Oliveto is Chair of the AI-Theory Lab at SUSTech. His main research interest is the rigorous performance analysis of bio-inspired computation techniques. Further information can be accessed via his personal webpage: https://peteroliveto.github.io
Key Words
Artificial Intelligence, Bio-Inspired Computation, Theory
Pietro Oliveto
Professor of Computer Science