NexTech 2021 Congress
October 03, 2021 to October 07, 2021 - Barcelona, Spain

  • UBICOMM 2021, The Fifteenth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies
  • ADVCOMP 2021, The Fifteenth International Conference on Advanced Engineering Computing and Applications in Sciences
  • SEMAPRO 2021, The Fifteenth International Conference on Advances in Semantic Processing
  • AMBIENT 2021, The Eleventh International Conference on Ambient Computing, Applications, Services and Technologies
  • EMERGING 2021, The Thirteenth International Conference on Emerging Networks and Systems Intelligence
  • DATA ANALYTICS 2021, The Tenth International Conference on Data Analytics
  • GLOBAL HEALTH 2021, The Tenth International Conference on Global Health Challenges
  • CYBER 2021, The Sixth International Conference on Cyber-Technologies and Cyber-Systems

SoftNet 2021 Congress
October 03, 2021 to October 07, 2021 - Barcelona, Spain

  • ICSEA 2021, The Sixteenth International Conference on Software Engineering Advances
  • ICSNC 2021, The Sixteenth International Conference on Systems and Networks Communications
  • CENTRIC 2021, The Fourteenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services
  • VALID 2021, The Thirteenth International Conference on Advances in System Testing and Validation Lifecycle
  • SIMUL 2021, The Thirteenth International Conference on Advances in System Simulation
  • SOTICS 2021, The Eleventh International Conference on Social Media Technologies, Communication, and Informatics
  • INNOV 2021, The Tenth International Conference on Communications, Computation, Networks and Technologies
  • HEALTHINFO 2021, The Sixth International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing

NetWare 2021 Congress
November 14, 2021 to November 18, 2021 - Athens, Greece

  • SENSORCOMM 2021, The Fifteenth International Conference on Sensor Technologies and Applications
  • SENSORDEVICES 2021, The Twelfth International Conference on Sensor Device Technologies and Applications
  • SECURWARE 2021, The Fifteenth International Conference on Emerging Security Information, Systems and Technologies
  • AFIN 2021, The Thirteenth International Conference on Advances in Future Internet
  • CENICS 2021, The Fourteenth International Conference on Advances in Circuits, Electronics and Micro-electronics
  • ICQNM 2021, The Fifteenth International Conference on Quantum, Nano/Bio, and Micro Technologies
  • FASSI 2021, The Seventh International Conference on Fundamentals and Advances in Software Systems Integration
  • GREEN 2021, The Sixth International Conference on Green Communications, Computing and Technologies

TrendNews 2021 Congress
November 14, 2021 to November 18, 2021 - Athens, Greece

  • CORETA 2021, Advances on Core Technologies and Applications
  • DIGITAL 2021, Advances on Societal Digital Transformation

 


ThinkMind // International Journal On Advances in Software, volume 4, numbers 1 and 2, 2011 // View article soft_v4_n12_2011_1


Stochastic Greedy Algorithms: A leaning based approach to combinatorial optimization

Authors:
Viswa Viswanathan
Anup Sen
Soumyakanti Chakraborty

Keywords: greedy algorithms; stochastic approaches; approximate solutions; knapsack problem; combinatorial auctions; single-machine scheduling; machine learning

Abstract:
Research in combinatorial optimization initially focused on finding optimal solutions to various problems. Researchers realized the importance of alternative approaches when faced with large practical problems that took too long to solve optimally and this led to approaches like simulated annealing and genetic algorithms which could not guarantee optimality, but yielded good solutions within a reasonable amount of computing time. In this paper we report on our experiments with stochastic greedy algorithms (SGA) – perturbed versions of standard greedy algorithms. SGA incorporates the novel idea of learning from optimal solutions, inspired by data-mining and other learning approaches. SGA learns some characteristics of optimal solutions and then applies them while generating its solutions. We report results based on applying this approach to three different problems – knapsack, combinatorial auctions and single-machine job sequencing. Overall, the method consistently produces solutions significantly closer to optimal than standard greedy approaches. SGA can be seen in the space of approximate algorithms as falling between the very quick greedy approaches and the relatively slower soft computing approaches like genetic algorithms and simulated annealing. SGA is easy to understand and implement -- once a greedy solution approach is known for a problem, it becomes possible to very quickly rig up a SGA for the problem. SGA has explored only one aspect of learning from optimal solutions. We believe that there is a lot of scope for variations on the theme, and the broad idea of learning from optimal solutions opens up possibilities for new streams of research.

Pages: 1 to 11

Copyright: Copyright (c) to authors, 2011. Used with permission.

Publication date: September 15, 2011

Published in: journal

ISSN: 1942-2628

SERVICES CONTACT
2010 - 2017 © ThinkMind. All rights reserved.
Read Terms of Service and Privacy Policy.