DescriptionGenetic Algorithms (GAs) are metaheuristic algorithm operates on encoding mechanism. GA’s success depends on a good balance between exploration and exploitation. Exploration aims to visit entirely new region of a search space whilst exploitation focuses on those regions of a search space recently visited. The key challenges with the metaheuristic algorithms including GAs are: (a) how to improve the convergence speed and avoid premature convergence, (b) mechanism for controlling parameters and, (c) how to maintain a good balance between exploration and exploitation. The aim of this talk is to provide a much deeper understanding about exploration and exploitation strategies and identify possibilities by which performance of the GAs can be improved. The domain of enquiry in this talk is grammatical inference (GI). GI is a methodology to infer context free grammars (CFGs) from training data and, it provides greater benefits in data mining and big data analytics. Through this talk an attempt is made to present a fresh treatment and discuss the utility of GA and GI for big data analytics in 4-rational aspects: (a) what are challenges in implementing a GA; (b) how to control exploration and exploitation in GA; (c) How to apply GA for real word problems and, (d) How GA and GI are suitable for big data analytics. The obvious outcome of this talk is to create an awareness among the researchers about the suitability of GA and GI for big data analytics. I also believe that this talk might be useful for the researcher to identify the possibilities to develop new algorithm in the big data analytics domain.
|10 Aug 2019
|Indian Institute of Information Technology, Allahabad, India
|Degree of Recognition