Bio-inspired computing techniques have been shown to be powerful approximation solvers for sophisticated optimization problems in practice. During the past two decades, there are also a lot of efforts on theoretical analysis of these techniques, particularly on analyzing their running time complexity until finding an optimal or approximate solution of a given optimization problem. The theoretical results can help practitioners better understand the working principles of bio-inspired computing techniques, and thus, design efficient algorithms. However, compared with the great success in practice, the theoretical foundation of bio-inspired computing techniques is still weak.
The primary aim of this special session is to bring together researchers working on theoretical analysis of bio-inspired computing techniques, and to provide a forum for them to discuss the latest outcomes and new directions.
The original contributions in the theory of bio-inspired computation are welcome. Topics of interest include, but are not limited to:
- General analytical methods like drift analysis
- Exact and approximation runtime analysis
- Parameterized complexity analysis
- Black box complexity
- Dynamic and static parameter choices
- Population diversity
- Population dynamics
- Fitness landscape and problem difficulty analysis
- All problem domains will be considered including:
- combinatorial and continuous optimization
- single-objective and multi-objective optimization
- constraint handling
- dynamic and stochastic optimization
- co-evolution and evolutionary learning
In addition to rigorous mathematical investigations, carefully crafted experimental studies contributing towards the theoretical foundations of bio-inspired computation are also welcome.
- Submission deadline: 31 January 2021
- Notification: 22 March 2021
- Final paper submission: 7 April 2021
*Supported by IEEE CIS Task Force on Theoretical Foundations of Bio-inspired Computation