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Understanding non-modular functionality – lessons from genetic algorithms

Kuorikoski, Jaakko and Pöyhönen, Samuli (2012) Understanding non-modular functionality – lessons from genetic algorithms. In: UNSPECIFIED.

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Abstract

Evolution is often characterized as a tinkerer that creates efficient but messy solutions to problems. We analyze the nature of the problems that arise when we try to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem – solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show that the reason for why evolutionary designs are often hard to understand is that they exhibit non-modular functionality, and that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Kuorikoski, Jaakkojaakko.kuorikoski@helsinki.fi
Pöyhönen, Samulisamuli.poyhonen@helsinki.fi
Keywords: modularity; understanding; evolutionary algorithms; cognitive science
Subjects: Specific Sciences > Cognitive Science
General Issues > Explanation
Depositing User: Jaakko Kuorikoski
Date Deposited: 16 Oct 2012 12:43
Last Modified: 16 Oct 2012 12:43
Item ID: 9373
URI: http://philsci-archive.pitt.edu/id/eprint/9373

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