Finding multi-objective paths in stochastic networks: A simulation-based genetic algorithm approach

Zhaowang Ji, Anthony Chen, Kitti Subprasom

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

14 Citations (Scopus)


Path finding is a fundamental research topic in transportation due to its wide applications in transportation planning and Intelligent Transportation System (ITS). In transportation, the path finding problem is usually defined as the shortest path (SP) problem in terms of distance, time, cost, or a combination of criteria under a deterministic environment. However, in real life situations, the environment is often uncertain. In this paper, we develop a simulation-based genetic algorithm to find multi-objective paths in stochastic networks. Numerical experiments are presented to demonstrate the algorithm feasibility.
Original languageEnglish
Title of host publicationProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Number of pages7
Publication statusPublished - 13 Sep 2004
Externally publishedYes
EventProceedings of the 2004 Congress on Evolutionary Computation, CEC2004 - Portland, OR, United States
Duration: 19 Jun 200423 Jun 2004


ConferenceProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Country/TerritoryUnited States
CityPortland, OR

ASJC Scopus subject areas

  • Engineering(all)

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