The Grouping Genetic Algorithm

  • Emanuel Falkenauer
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 7)


An important class of difficult optimization problems are grouping problems, where the aim is to group together members of a set (i.e. find a good partition of the set). In this paper we present the Grouping Genetic Algorithm (GGA), which is a Genetic Algorithm (GA) heavily modified to suit the structure of grouping problems. We first show why both the standard and the ordering GAs fare poorly in this domain, by pointing out their inherent difficulty to capture the regularities of the functional landscape of the grouping problems. We then propose a new encoding scheme and genetic operators adapted to these problems, embodied by the GGA. An experimental evaluation of the GGA on several different problems shows its superiority over standard GAs when applied to grouping problems. The potential of the algorithm is further illustrated by its application to the Bin Packing Problem, where a hybridised GGA outperforms one of the best Operations Research techniques to date.


Genetic Algorithm Encode Scheme Crossover Operator Genetic Operator Hard Constraint 
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

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Emanuel Falkenauer
    • 1
  1. 1.Department of Industrial AutomationCRIF — Research Centre for Belgian Metalworking IndustryBrusselsBelgium

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