Work

Running genetic algorithms on Hadoop for solving high dimensional optimization problems

Year: 2015

Type: conference-paper

Abstract: Hadoop is a popular MapReduce framework for developing parallel applications in distributed environments. Several advantages of MapReduce such as programming ease and ability to use commodity hardware make the applicability of soft computing methods for parallel and distributed systems easier than before. In this paper, we present the results of an experimental study on running soft computing algorithms using Hadoop. This study shows how a simple genetic algorithm running on Hadoop can be used to produce solutions for high dimensional optimization problems. In addition, a simple but effective technique, which did not need MapReduce chains, has been proposed.

Source: International Conference on Application of Information and Communication Technologies

DOI: 10.1109/icaict.2015.7338506

Authors: Gungor Yildirim, Ibrahim R. Hallac, Galip Aydin, Yetkin Tatar

Institution: Fırat University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2175467478

FWCI: 0.188

Cites: 12

Cited by: 8

Related to: 10