Maumita Bhattacharya

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Genetic programming (GP), a relatively young and growing branch of evolutionary computation is gradually proving to be a promising method of modelling complex prediction and classification problems. This paper evaluates the suitability of a linear genetic programming (LGP) technique to predict electricity demand in the State of Victoria, (More)
Bring Your Own Device (BYOD) is a rapidly growing trend in businesses concerned with information technology. BYOD presents a unique list of security concerns for businesses implementing BYOD policies. Recent publications indicate a definite awareness of risks involved in incorporating BYOD into business, however it is still an underrated issue compared to(More)
Premature convergence to suboptimal solutions is one of the prime concerns of using evolutionary algorithms (EA) in high complexity real world optimization problems. As the evolutionary search progresses, it is important to avoid reaching a state where the genetic operators can no longer produce superior offspring while striking a balance between(More)
As the evolutionary search progresses, it is important to avoid reaching a state where the genetic operators can no longer produce superior offspring, prematurely. This is likely to occur when the search space reaches a homogeneous or near-homogeneous configuration converging to a local optimal solution. Maintaining a certain degree of population diversity(More)
Stochastic search techniques such as evolutionary algorithms (EA) are known to be better explorer of search space as compared to conventional techniques including deterministic methods. However, in the era of big data like most other search methods and learning algorithms, suitability of evolutionary algorithms is naturally questioned. Big data pose new(More)