Improving the effectiveness of genetic programming using continuous self-adaptation

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Abstract

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.

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Publication date19 Apr 2018
Publication titleArtificial Life and Intelligent Agents - Second International Symposium, ALIA 2016, Revised Selected Papers
PublisherSpringer
Pages97-102
Number of pages6
Volume732
ISBN (Print)9783319904177
Original languageEnglish
Event2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016 - Birmingham, United Kingdom
Duration: 14 Jun 201615 Jun 2016

Publication series

NameCommunications in Computer and Information Science
Volume732
ISSN (Print)1865-0929

Conference

Conference2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016
CountryUnited Kingdom
CityBirmingham
Period14/06/1615/06/16

    Keywords

  • Benchmarks, Genetic programming, Self-adaptation, Tartarus

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