Title

Revising The Evolutionary Computation Abstraction: Minimal Criteria Novelty Search

Keywords

Artificial life; Evolution of complexity; NEAT; Novelty search

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. Copyright 2010 ACM.

Publication Date

8-27-2010

Publication Title

Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Number of Pages

1031-1038

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1830483.1830503

Socpus ID

77955883379 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77955883379

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