Structuring pattern generalization through Evolutionary Techniques

Ahmet Ugur, Michael Conrad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


The capabilities built into a processing network control the manner in which it generalizes from a training set and therefore how it groups environmental patterns. These capabilities are developed through learning, ultimately evolutionary learning, and therefore have an objective basis in so far as the grouping tendencies afford a selective advantage. But for this development to occur it is necessary that the processing network in fact be able to evolve grouping tendencies that reflect selective pressures. The extent to which this is possible depends on how wide a variety of grouping dynamics the processing network can support (its dynamic richness) and on whether its structure-function gradualism (evolutionary friendliness) is sufficient to provide access to these grouping responses through a variation-selection process. We describe a "softened" cellular automaton model that illustrates how different grouping responses can be evolved in cases simple enough to examine the entire test set.

Original languageEnglish
Title of host publicationEvolutionary Programming VI - 6th International Conference, EP 1997, Proceedings
EditorsPeter J. Angeline, Robert G. Reynolds, John R. McDonnell, Russ Eberhart
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783540627883
StatePublished - 1997
Event6th International Conference on Evolutionary Programming, EP 1997 - Indianapolis, United States
Duration: Apr 13 1997Apr 16 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Conference on Evolutionary Programming, EP 1997
Country/TerritoryUnited States


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