Educational background and field(s) of interest
Anil’s main research interests include topics in computational intelligence, specifically naturally inspired artificial intelligence algorithms specializing in evolutionary computation (EC) and artificial neural networks (ANNs). His current research focuses on Neuroevolution a field that aims to optimize ANNs using EC. Particularly, he studies scalability in evolutionary computing for large scale optimization, self-adaptive parameter selection and biologically inspired learning in ANNs in agent-based reinforcement learning scenarios.
What is your motivation to contribute to The Phoenix Project?
It has always been a great inspiration to me to study the emergence of intelligent behaviour in nature and try to mimic this in artificial systems. In Phoenix, we aim to mimic the process of biological evolution to produce a swarm of agents to solve environment exploration and monitoring tasks. It is very interesting to observe the output of the evolutionary computing methods and compare them with the results produced by expert knowledge.
Moreover, it is very stimulating to work within an interdisciplinary team that specializes different aspects of the project.