Maumita Bhattacharya

Learn More
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)
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)
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)