Skip to main navigation Skip to search Skip to main content

Probabilistic Based Evolutionary Optimizers in Bi-objective Travelling Salesman Problem

Vui Ann Shim, Kay Chen Tan, Jun Yong Chia

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

This paper studies the probabilistic based evolutionary algorithms in dealing with bi-objective travelling salesman problem. Multi-objective restricted Boltzmann machine and univariate marginal distribution algorithm in binary representation are modified into permutation based representation. Each city is represented by an integer number and the probability distributions of the cities are constructed by running the modeling approach. A refinement operator and a local exploitation operator are proposed in this work. The probabilistic based evolutionary optimizers are subsequently combined with genetic based evolutionary optimizer to complement the limitations of both algorithms.
Original languageEnglish
Title of host publicationSimulated Evolution and Learning
Subtitle of host publication8th International Conference, SEAL 2010, Kanpur, India, December 1-4, 2010: Proceedings
EditorsKalyanmoy Deb
PublisherSpringer 
Pages588-592
ISBN (Print)9783642172977
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event8th Simulated Evolution and Learning Conference (SEAL 2010) - Indian Institute of Technology Kanpur, Kanpur, India
Duration: 1 Dec 20104 Dec 2010

Publication series

NameLecture Notes in Computer Science
Volume6457
ISSN (Print)0302-9743

Conference

Conference8th Simulated Evolution and Learning Conference (SEAL 2010)
PlaceIndia
CityKanpur
Period1/12/104/12/10

Research Keywords

  • Estimation of distribution algorithm
  • evolutionary multi-objective optimization
  • restricted Boltzmann machine
  • travelling salesman problem

Fingerprint

Dive into the research topics of 'Probabilistic Based Evolutionary Optimizers in Bi-objective Travelling Salesman Problem'. Together they form a unique fingerprint.

Cite this