Iterative Prototype Optimisation with Evolved Improvement Steps

  • Jiri Kubalik
  • Jan Faigl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3905)


Evolutionary algorithms have already been more or less successfully applied to a wide range of optimisation problems. Typically, they are used to evolve a population of complete candidate solutions to a given problem, which can be further refined by some problem-specific heuristic algorithm. In this paper, we introduce a new framework called Iterative Prototype Optimisation with Evolved Improvement Steps. This is a general optimisation framework, where an initial prototype solution is being improved iteration by iteration. In each iteration, a sequence of actions/operations, which improves the current prototype the most, is found by an evolutionary algorithm. The proposed algorithm has been tested on problems from two different optimisation problem domains – binary string optimisation and the traveling salesman problem. Results show that the concept can be used to solve hard problems of big size reliably achieving comparably good or better results than classical evolutionary algorithms and other selected methods.


Genetic Algorithm Evolutionary Algorithm Travel Salesman Problem Tournament Selection Simple Genetic Algorithm 
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 2006

Authors and Affiliations

  • Jiri Kubalik
    • 1
  • Jan Faigl
    • 1
  1. 1.Department of CyberneticsCTU Prague, Technicka 2Prague 6Czech Republic

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