Skip to main content

Research Repository

Advanced Search

Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm

Steyven, Andreas; Hart, Emma; Paechter, Ben

Authors



Abstract

It is well known that in open-ended evolution, the nature of the environment plays in key role in directing evolution. However, in Evolutionary Robotics, it is often unclear exactly how parameterisation of a given environment might influence the emergence of particular behaviours. We consider environments in which the total amount of energy is parameterised by availability and value, and use surface plots to explore the relationship between those environment parameters and emergent behaviour using a variant of a well-known distributed evolutionary algorithm (mEDEA). Analysis of the resulting landscape show that it is crucial for a researcher to select appropriate parameterisations in order that the environment provides the right balance between facilitating survival and exerting sufficient pressure for new behaviours to emerge. To the best of our knowledge, this is the fi rst time such an analysis has been undertaken.

Presentation Conference Type Conference Paper (Published)
Conference Name PPSN 2016 14th International Conference on Parallel Problem Solving from Nature
Start Date Oct 17, 2016
End Date Oct 21, 2016
Acceptance Date May 30, 2016
Online Publication Date Aug 31, 2016
Publication Date 2016
Deposit Date Jun 1, 2016
Publicly Available Date Oct 3, 2016
Electronic ISSN 1611-3349
Publisher Springer
Peer Reviewed Peer Reviewed
Pages 921-931
Series Title Lecture Notes in Computer Science
Series Number 9921
Series ISSN 0302-9743
Book Title Parallel Problem Solving from Nature – PPSN XIV; Lecture Notes in Computer Science
ISBN 9783319458229
DOI https://doi.org/10.1007/978-3-319-45823-6_86
Keywords Evolutionary algorithm; evolutionary robotics; energy zones;
Public URL http://researchrepository.napier.ac.uk/id/eprint/10331
Contract Date Oct 3, 2016

Files

Understanding environmental influence in an Open-ended evolutionary algorithm. (875 Kb)
PDF







You might also like



Downloadable Citations