I have genuine interest in Machine Learning in general and in Genetic Programming (GP) in particular.
GP is a powerful learning engine that inspired by the theory of evolution and our contemporary understanding of biology and natural evolution. In the past few decades, researchers have investigated the applications of GP in several domains such as: Finance, signal analysis, multi-objective optimisation. Hence, the use of GP in these domains has shown remarkable performance in the sense that new solutions have been evolved that human never thought about.
I am interested in the application of GP in new real world problems, such as, pattern recognition, resource allocation, data forecasting, and analysing stochastic data behaviour. However, my interest is not only in obtaining a solution to the given task; I am also interested in simplifying and understanding as much as possible the evolved solutions in order to gain a new knowledge about the given problem. Moreover, I enjoy working on the theory of the GP and explore the behaviour of evolution under different circumstances. In particular, I am interested in investigating methods of improving the evolutionary process undertaken by the GP to improve its performance. Naturally, I am keen to keep my knowledge up to date in the other aspects and topics of Machine Learning as well as general topics in Computer Science.
My main contributions to science have been in the field of Evolutionary Algorithms (EAs) and allied disciplines more specially, in the areas of Genetic Programming, Genetic Algorithms, Particle Swarm Optimisation, Signal Analysis, Data Mining, Data Compression, Multi-agent systems, Surrogate modelling, Game theory, and Mixture of Experts.
Overall, I am interested in contributing to the field of Machine Learning and develop smarter applications to solve complex problems and enhance our life style.