An Overview of a Synergetic Combination of Local Search with Evolutionary Learning to Solve Optimization Problems

  • Rasiah Loganantharaj
  • Bushrod Thomas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1821)


We describe a method for solving combinatorial optimization problem that combines best aspects of local search and genetic algorithms. We formulate combinatorial optimization problems as state space search problems. While local search methods, such as hill climbing, are computationally efficient, they suffers from local minima traps. Global search methods are guaranteed to find optimal solutions, but are not always feasible. We favor a polynomial time technique that delivers solutions closer to optimal by modifying the search space of the local search method. We demonstrate our strategy on a single-machine scheduling problem with two objective functions: (1) minimizing average job completion time, and (2) minimizing total tardiness. We apply the technique to optimally schedule the robot arm of an automated retrieval system. Obtaining optimal solutions to such scheduling problems is computationally intractable, but experimental results show our technique produces better solutions than those found by genetic algorithm with random key encoding.


Schedule Problem Local Search Weight Vector Combinatorial Optimization Problem Hill Climbing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Boyan, J.: Learning Evaluation Functions for Global Optimization and Boolean Satisfiability. Proceedings of AAAI (1998)Google Scholar
  2. 2.
    Davis, L.: Genetic Algorithms and Simulated Annealing, Research Notes in Artificial Intelligence. Morgan Kaufmann, New York (1987)zbMATHGoogle Scholar
  3. 3.
    Du, J. Leung, J.Y.: Minimizing Total Tardiness on One Machine is NP-Hard. Mathematics of Operations Research, Vol. 15 (1990) 483–495zbMATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    Grass J., Zilberstein, S.: Anytime Algorithm Development Tool. Sigart Bulletin, Vol. 7, No. 2 (1996)Google Scholar
  5. 5.
    Leon, V.J., Balakrishnan, R.: Strength and Adaptability of Problem-Space based Neighborhoods for Resource-constrained Scheduling. OR Spektrum, Vol 17. Springer-Verlag, Berlin Heidelberg New York (1995) 172–182Google Scholar
  6. 6.
    Loganantharaj R., Thomas, B.: Improving the Efficiency of the Ground Processing Scheduling System. NASA/ASEE Summer Faculty Research Report (1997)Google Scholar
  7. 7.
    Norman, B.A., Bean, J.C.: Random Keys Genetic Algorithm for Job Shop Scheduling. Technical Report 94-5, Dept. of Industrial and Operations Engineering, The University of Michigan (1994)Google Scholar
  8. 8.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, Third Revised and Extended Edition, Springer (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Rasiah Loganantharaj
    • 1
  • Bushrod Thomas
    • 1
  1. 1.Center for Advanced Computer StudiesUniversity of LouisianaLafayette

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