Dynamic fitness landscapes

Seth Bullock

Dynamic fitness landscapes

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

Genetic Algorithms (GAs) are typically thought to work on static fitness landscapes. In contrast, natural evolution works on fitness landscapes that change over evolutionary time as a result of co-evolution. Sexual selection and predator-prey evolution are examined as clear examples of phenomena that transform fitness landscapes. The concept of co-evolution is subsequently defined, before attempts to utilise co-evolution in the use of GAs as design tools are reviewed and speculations concerning future applications of automatic co-evolutionary techniques for design are considered.

Original language	Undefined/Unknown
Title of host publication	The Seventh White House Papers: Graduate Research in the Cognitive Computing Sciences at Sussex
Editors	Peter de Bourcier, Ronald Lemmen, Adrian Thompson
Publisher	University of Sussex
Publication status	Published - 1995

Access to Document

http://seis.bris.ac.uk/~sb15704/pubs_abstracts.html#eps261470

Handle.net

Persistent link

Cite this

@inproceedings{9a49e791bf4446898be2a1cfb0122b6b,

title = "Dynamic fitness landscapes",

abstract = "Genetic Algorithms (GAs) are typically thought to work on static fitness landscapes. In contrast, natural evolution works on fitness landscapes that change over evolutionary time as a result of co-evolution. Sexual selection and predator-prey evolution are examined as clear examples of phenomena that transform fitness landscapes. The concept of co-evolution is subsequently defined, before attempts to utilise co-evolution in the use of GAs as design tools are reviewed and speculations concerning future applications of automatic co-evolutionary techniques for design are considered.",

author = "Seth Bullock",

year = "1995",

language = "Undefined/Unknown",

editor = "Bourcier, {Peter de} and Ronald Lemmen and Adrian Thompson",

booktitle = "The Seventh White House Papers: Graduate Research in the Cognitive Computing Sciences at Sussex",

publisher = "University of Sussex",

}

TY - GEN

T1 - Dynamic fitness landscapes

AU - Bullock, Seth

PY - 1995

Y1 - 1995

N2 - Genetic Algorithms (GAs) are typically thought to work on static fitness landscapes. In contrast, natural evolution works on fitness landscapes that change over evolutionary time as a result of co-evolution. Sexual selection and predator-prey evolution are examined as clear examples of phenomena that transform fitness landscapes. The concept of co-evolution is subsequently defined, before attempts to utilise co-evolution in the use of GAs as design tools are reviewed and speculations concerning future applications of automatic co-evolutionary techniques for design are considered.

AB - Genetic Algorithms (GAs) are typically thought to work on static fitness landscapes. In contrast, natural evolution works on fitness landscapes that change over evolutionary time as a result of co-evolution. Sexual selection and predator-prey evolution are examined as clear examples of phenomena that transform fitness landscapes. The concept of co-evolution is subsequently defined, before attempts to utilise co-evolution in the use of GAs as design tools are reviewed and speculations concerning future applications of automatic co-evolutionary techniques for design are considered.

M3 - Conference Contribution (Conference Proceeding)

BT - The Seventh White House Papers: Graduate Research in the Cognitive Computing Sciences at Sussex

A2 - Bourcier, Peter de

A2 - Lemmen, Ronald

A2 - Thompson, Adrian

PB - University of Sussex

ER -