Improving the Effectiveness of Genetic Programming Using Continuous Self-adaptation

  • Thomas D. GriffithsEmail author
  • Anikó Ekárt
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 732)


Genetic Programming (GP) is a form of nature-inspired computing, introduced over 30 years ago, with notable success in problems such as symbolic regression. However, there remains a lot of relatively unexploited potential for solving hard, real-world problems. There is consensus in the GP community that the lack of effective real-world benchmark problems negatively impacts the quality of research [4]. When a GP system is initialised, a number of parameters must be provided. The optimal setup configuration is often not known, due to the fact that many of the values are problem and domain specific, meaning the GP system is unable to produce satisfactory results. We believe that the implementation of continuous self-adaptation, along with the introduction of tunable and suitably difficult benchmark problems, will allow for the creation of more robust GP systems that are resilient to failure.


Genetic Programming Self-adaptation Benchmarks Tartarus 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Aston Lab for Intelligent Collectives Engineering (ALICE)Aston UniversityBirminghamUK

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