Applying Genetic Algorithms to Real-World Problems

  • Emanuel Falkenauer
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 111)


This paper outlines what the author perceives as crucial ingredients of a successful application of Genetic Algorithms (GAs) to real-world combinatorial problems. First, the importance of the Schema Theorem is stressed, pointing to crossover as the most potent force in a GA. Second, the importance of an encoding and operators adapted to the problem being solved is demonstrated, with two implications: the importance of the binary alphabet has been largely overstated in the past (in many problems it is not only unwarranted, it is detrimental), and practical GAS must be built to solve problems (i.e., sets of instances) rather than (arbitrary) functions.

The benefits of the above guidelines are illustrated by the Grouping GA (GGA), applied to three different grouping problems, namely Bin Packing and its variety Line Balancing, Equal Piles and Economies of Scale. The first application suggests a superiority of crossover-based search over a classic Branch & Bound, the second shows the superiority of the GGA over standard GAs applied to grouping problems, and the third illustrates the kind of industrial applications GAS can be called upon to solve.


Genetic Algorithm Tabu Search Crossover Operator Line Balance Standard Crossover 
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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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Emanuel Falkenauer
    • 1
  1. 1.Department of Applied MechanicsBrussels University (ULB)BrusselsBelgium

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