Maumita Bhattacharya

BYOD Security: A New Business Challenge

— 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)

A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria

Maumita Bhattacharya, Ajith Abraham, Baikunth Nath
HIS
2001

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, Australia, while… (More)

Exploiting Landscape Information to Avoid Premature Convergence in Evolutionary Search

Maumita Bhattacharya
IEEE Congress on Evolutionary Computation
2006

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 exploration and… (More)

Intelligent financial fraud detection: A comprehensive review

Jarrod West, Maumita Bhattacharya
Computers & Security
2016

Reduced computation for evolutionary optimization in noisy environment

Evolutionary Algorithms' (EAs') application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on the performance of any population based search technique such as EA. Estimating the fitness of individuals instead of actually evaluating them is a workable approach to deal with this… (More)

An informed operator approach to tackle diversity constraints in evolutionary search

Maumita Bhattacharya
International Conference on Information…
2004

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)

Surrogate based EA for expensive optimization problems

Maumita Bhattacharya
IEEE Congress on Evolutionary Computation
2007

The 2001 International Conference on Computational Science

David Walker, Kenneth Tan, +10 authors Maumita Bhattacharya
2001

Expensive optimization, uncertain environment: an EA-based solution

Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations.allUse of any population based iterative technique such as evolutionary algorithmall in such problem domains is thus practically prohibitive. A feasible alternative is… (More)

An Investigation on Two Surrogate-Based EAs

Maumita Bhattacharya
Austr. J. Intelligent Information Processing…
2010

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