About Research Cluster

Nature inspired computation is the study of computational systems that use ideas and get inspiration from natural systems, including biological, social, economical, ecological, physical and chemical systems. It is an emerging interdisciplinary field in which a range of techniques and methods are studied for dealing with large, complex and dynamic problems. The focus of this cluster is to investigate various nature inspired approaches to tackling large, complex and dynamic problems in the real world. Some of the research issues that are covered by this cluster include, but not limited to:

• How to decompose a large and complex problem adaptively and automatically in a knowledge lean situation? How can populations, e.g., species or ensembles, be harnessed in dealing with large and complex problems?
• What are the theoretical foundations for studying problem decomposition?
• What is the computational time complexity of nature inspired algorithms? What is their real power (theoretically)?
• How do simple parts interact with each other to give a global solution? Is "modelling" a search landscape through a population, such as Estimation of Distribution Algorithms (EDA), an answer?
• How do artificial agents interact in an artificial market? What would be an optimal market mechanism in an artificial world?
• How can we use nature inspired techniques in designing and evolving artificial systems that are inherently fault-tolerence?
• How can we learn and evolve strategies for solving complex problems without human intervention and without pre-defined human knowledge?
• How can adaptively learn and evolve appropriate mix of heuristics for different real world problems?
• What can we learn from Nature in dealing with multi-ojectivity, constraints and uncertainty?