Using a Genetic Algorithm to Solve the Generalized Orienteering Problem

Part of the Operations Research/Computer Science Interfaces book series (ORCS, volume 43)


In this chapter, we use genetic algorithms (GAs) to solve the generalized orienteering problem (GOP). In the orienteering problem (OP), we are given a transportation network in which a start point and an end point are specified, and other points have associated scores. Given a fixed amount of time, the goal is to determine a path from start to end through a subset of the other locations in order to maximize the total path score. In the GOP, each point has a score with respect to a number of attributes (e.g., natural beauty, historical significance, cultural and educational attractions, and business opportunities) and the overall objective function is nonlinear. The GOP is more difficult than the OP, which is itself NP-hard. An effective heuristic using artificial neural networks (ANNs), however, has been designed to solve the GOP. In this chapter, we show that a straightforward GA can yield comparable results.

Key words

Generalized orienteering orienteering problem genetic algorithm 


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of MarylandCollege Park
  2. 2.Department of Decision and Information TechnologiesUniversity of MarylandMD 20742
  3. 3.Kogod School of BusinessAmerican UniversityWashington

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