arXiv subjects

Drona Pratap Chandu

Publications and source records attributed to Drona Pratap Chandu.

4 recordsLinked to original sources

arXiv2015

Big Step Greedy Algorithm for Maximum Coverage Problem

This paper proposes a greedy heuristic named as Big step greedy heuristic and investigates the application of Big step greedy heuristic for maximum k-coverage problem. Greedy algorithms construct the solution in multiple steps, the classical greedy algorithm for maximum k-coverage problem, in each step selects one set that contains the greatest number of uncovered elements. The Big step greedy heuristic, in each step selects p (1 <= p <= k) sets such that the union of selected p sets contains the greatest number of uncovered elements by evaluating all possible p-combinations of given sets. When p=k Big step greedy algorithm behaves like exact algorithm that computes optimal solution by evaluating all possible k-combinations of given sets. When p=1 it behaves like the classical greedy algorithm.

cs.DS↗

arXiv2015

Improved Greedy Algorithm for Set Covering Problem

This paper proposes a greedy algorithm named as Big step greedy set cover algorithm to compute approximate minimum set cover. The Big step greedy algorithm, in each step selects p sets such that the union of selected p sets contains greatest number of uncovered elements and adds the selected p sets to partial set cover. The process of adding p sets is repeated until all the elements are covered. When p=1 it behaves like the classical greedy algorithm.

cs.DS↗

arXiv2014

A Parallel Genetic Algorithm for Generalized Vertex Cover Problem

This paper presents a parallel genetic algorithm for generalised vertex cover problem (GVCP) using Hadoop Map-Reduce framework. The proposed Map-Reduce implementation helps to run the genetic algorithm for generalized vertex cover problem (GVCP) on multiple machines parallely and computes the solution in relatively short time.

cs.DC↗

arXiv2014

A Parallel Genetic Algorithm for Three Dimensional Bin Packing with Heterogeneous Bins

This paper presents a parallel genetic algorithm for three dimensional bin packing with heterogeneous bins using Hadoop Map-Reduce framework. The most common three dimensional bin packing problem which packs given set of boxes into minimum number of equal sized bins is proven to be NP Hard. The variation of three dimensional bin packing problem that allows heterogeneous bin sizes and rotation of boxes is computationally more harder than common three dimensional bin packing problem. The proposed Map-Reduce implementation helps to run the genetic algorithm for three dimensional bin packing with heterogeneous bins on multiple machines parallely and computes the solution in relatively short time.

cs.DC↗