G
G. S. Mahapatra
Researcher at National Institute of Technology, Puducherry
Publications - 106
Citations - 1618
G. S. Mahapatra is an academic researcher from National Institute of Technology, Puducherry. The author has contributed to research in topics: Fuzzy logic & Computer science. The author has an hindex of 20, co-authored 82 publications receiving 1228 citations. Previous affiliations of G. S. Mahapatra include Siliguri Institute of Technology & Indian Institute of Engineering Science and Technology, Shibpur.
Papers
More filters
Journal ArticleDOI
An improved genetic algorithm based approach to solve constrained knapsack problem in fuzzy environment
TL;DR: An improved genetic algorithm (GA) is proposed to solve constrained knapsack problem in fuzzy environment using fuzzy formulation and two types of fuzzy systems, one is credibility measure and another is graded mean integration approach.
Journal ArticleDOI
Entropy based region reducing genetic algorithm for reliability redundancy allocation in interval environment
TL;DR: Comparative performance studies of the proposed subpopulation and entropy based region reducing genetic algorithm (GA) with Laplace crossover and power mutation demonstrate that the proposed GA is promising to solve the reliability redundancy optimization problem providing better optimum system reliability.
Journal ArticleDOI
A genetic ant colony optimization based algorithm for solid multiple travelling salesmen problem in fuzzy rough environment
TL;DR: The travelling cost is considered as imprecise in nature (fuzzy-rough) and is reduced to its approximate crisp using fuzzy-rough expectation.
Journal ArticleDOI
An Ant colony optimization approach for binary knapsack problem under fuzziness
TL;DR: Fuzzy possibility and necessity approaches are used to obtain optimal decision by the proposed ant colony algorithm and profit and weight are fuzzy in nature and taken as trapezoidal fuzzy number.
Journal ArticleDOI
An nhpp software reliability growth model with imperfect debugging and error generation
TL;DR: Experimental results show that the proposed model gives a better fit to the real failure data sets and predicts the future behavior of software development more accurately than the traditional SRGMs.