Evaluation of a Catalytic Search Algorithm

  • Lidia Yamamoto
Part of the Studies in Computational Intelligence book series (SCI, volume 284)


We investigate the search properties of pre-evolutionary random catalytic reaction networks, where reactions might be reversible, and replication is not taken for granted. Since it counts only on slow growth rates and weak selective pressure to steer the search process, catalytic search is an inherently slow process. However it presents interesting properties worth exploring, such as the potential to steer the computation flow towards good solutions, and to prevent premature convergence. We have designed a simple catalytic search algorithm, in order to assess its beamed search ability. In this paper we report preliminary results that show that although weak, the search strength achieved with catalytic search is sufficient to solve simple problems, and to find good approximations for more complex problems, while keeping a diversity of solutions and their building blocks in the population.


Genetic Algorithm Candidate Solution Reaction Network Membrane Computing Sanity Check 
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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lidia Yamamoto
    • 1
  1. 1.Computer Science DepartmentUniversity of BaselBaselSwitzerland

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