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Not Measuring Evolvability: Initial Investigation of an Evolutionary Robotics Search Space

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posted on 2023-06-07, 22:41 authored by Tom Smith, Phil HusbandsPhil Husbands, Michael O'Shea
Investigates the underlying search space of a difficult robotics problem. Previous work (P. Husbands et al., 1998) on the development of neural networks incorporating a model of gaseous neuromodulation (the GasNet) suggested that such networks are well-suited to evolutionary design for some problems. Networks that are allowed to use the gaseous signalling mechanism evolved significantly faster than networks with the mechanism disabled, implying a significant difference between the two search spaces. In this paper, we investigate this difference using a series of standard techniques for predicting the ¿difficulty¿ of searching in fitness landscapes. We show that, in this instance, measures based on random sampling do not discriminate between the two search spaces, due to the highly skewed nature of the fitness distributions, similar to those found in other difficult optimisation problems. It may be that such metrics are not useful as measures of difficulty for a class of complex problems.

History

Publication status

  • Published

Publisher

IEEE Computer Society

Pages

6.0

Presentation Type

  • paper

Event name

Proceedings of IEEE Congress on Evolutionary Computation 2001

Event location

Seoul, Korea

Event type

conference

ISBN

9780780366572

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

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