Genetic Programming Algorithms for Dynamic Environments

  • João Macedo
  • Ernesto Costa
  • Lino Marques
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9598)


Evolutionary algorithms are a family of stochastic search heuristics that include Genetic Algorithms (GA) and Genetic Programming (GP). Both GAs and GPs have been successful in many applications, mainly with static scenarios. However, many real world applications involve dynamic environments (DE). Many work has been made to adapt GAs to DEs, but only a few efforts in adapting GPs for this kind of environments. In this paper we present novel GP algorithms for dynamic environments and study their performance using three dynamic benchmark problems, from the areas of Symbolic Regression, Classification and Path Planning. Furthermore, we apply the best algorithm we found in the navigation of an Erratic Robot through a dynamic Santa Fe Ant Trail and compare its performance to the standard GP algorithm. The results, statistically validated, are very promising.


Evolutionary algorithms Genetic programing Dynamic environments 



This work was partially supported by the TIRAMISU project ( under grant agreement FP7/SEC/284747 and the MassGP project ( under grant agreement PTDC/EEI-CTP/2975/2012.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Systems and RoboticsCoimbraPortugal
  2. 2.Center of Informatics and Systems of the University of CoimbraUniversity of CoimbraCoimbraPortugal

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