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Revising the evolutionary computation abstraction: minimal criteria novelty search

Authors:

Kenneth O. StanleyAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 103 - 110

https://doi.org/10.1145/1830483.1830503

Published: 07 July 2010 Publication History

Abstract

Though based on abstractions of nature, current evolutionary algorithms and artificial life models lack the drive to complexity characteristic of natural evolution. Thus this paper argues that the prevalent fitness-pressure-based abstraction does not capture how natural evolution discovers complexity. Alternatively, this paper proposes that natural evolution can be abstracted as a process that discovers many ways to express the same functionality. That is, all successful organisms must meet the same minimal criteria of survival and reproduction. This abstraction leads to the key idea in this paper: Searching for novel ways of meeting the same minimal criteria, which is an accelerated model of this new abstraction, may be an effective search algorithm. Thus the existing novelty search method, which rewards any new behavior, is extended to enforce minimal criteria. Such minimal criteria novelty search prunes the space of viable behaviors and may often be more efficient than the search for novelty alone. In fact, when compared to the raw search for novelty and traditional fitness-based search in the two maze navigation experiments in this paper, minimal criteria novelty search evolves solutions more consistently. It is possible that refining the evolutionary computation abstraction in this way may lead to solving more ambitious problems and evolving more complex artificial organisms.

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Cited By

Silva DTorchelsen RAguiar M(2025)Procedural game level generation with GANs: potential, weaknesses, and unresolved challenges in the literatureMultimedia Tools and Applications10.1007/s11042-025-20612-9Online publication date: 18-Jan-2025
https://doi.org/10.1007/s11042-025-20612-9
Höfling LSzatko KBehrens CDeng YQiu YKlindt DJessen ZSchwartz GBethge MBerens PFranke KEcker AEuler T(2024)A chromatic feature detector in the retina signals visual context changeseLife10.7554/eLife.8686013Online publication date: 4-Oct-2024
https://doi.org/10.7554/eLife.86860
Stock MGorochowski T(2024)Open-endedness in synthetic biology: A route to continual innovation for biological designScience Advances10.1126/sciadv.adi362110:3Online publication date: 19-Jan-2024
https://doi.org/10.1126/sciadv.adi3621
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Index Terms

Revising the evolutionary computation abstraction: minimal criteria novelty search
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Copyright © 2010 ACM.

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Published: 07 July 2010

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https://doi.org/10.1007/s11042-025-20612-9
Höfling LSzatko KBehrens CDeng YQiu YKlindt DJessen ZSchwartz GBethge MBerens PFranke KEcker AEuler T(2024)A chromatic feature detector in the retina signals visual context changeseLife10.7554/eLife.8686013Online publication date: 4-Oct-2024
https://doi.org/10.7554/eLife.86860
Stock MGorochowski T(2024)Open-endedness in synthetic biology: A route to continual innovation for biological designScience Advances10.1126/sciadv.adi362110:3Online publication date: 19-Jan-2024
https://doi.org/10.1126/sciadv.adi3621
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