The area of runtime analysis for bio-inspired computing techniques provides new insights into the working behaviour of these methods for solving optimization problems. The theoretical analysis of these algorithms includes studying the runtime complexity with respect to the input size and/or other parameters of the instance of the problem until reaching an optimal or approximate solution of the problem. Rigorous analysis of these algorithms helps us in designing more efficient algorithms. Moreover, investigating the effect of different parameters of the studied algorithms leads to more efficient parameter tuning for these algorithms. Furthermore, studying the theoretical behaviour of bio-inspired algorithms with respect to the characteristics of the studied problem is beneficial in choosing the right algorithm for solving each instance of the problem.
Topics
The purpose of this special session is to bring together people working on theoretical analysis of bio-inspired computing techniques. We aim to provide a forum for the researchers in this field to discuss the latest outcomes and new directions in the theory of bio-inspired algorithms.
Topics of interest include, but not limited to:
- Theoretical foundations of bio-inspired heuristics
- Exact and approximation runtime analysis
- Parameterized complexity analysis
- Black box complexity
- Self-adaptation
- 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
Important Dates
- Submission deadline: 7 January 2019
- Notification: 7 March 2019
- Final paper submission: 31 March 2019
Organizers
Mojgan Pourhassan (mojgan.pourhassan@adelaide.edu.au) University of Adelaide, Australia
Frank Neumann (frank.neumann@adelaide.edu.au) University of Adelaide, Australia
Chao Qian (chaoqian@ustc.edu.cn) University of Science and Technology of China, China
*Supported by IEEE CIS Task Force on Theoretical Foundations of Bio-inspired Computation