Scouting Context-sensitive Components
Scouting Context-sensitive Components
Nature's gadgets are implemented without being planned and therefore can utilize context-sensitive components. Thus functionality that would require extensive networks of context-free components can be elicited from a minimum of material. Proteins can serve as context-sensitive components for pattern processing applications. We here describe an evolutionary search strategy currently under investigation for its potential use in conjunction with computer controlled fluidics to evaluate the computational capabilities of proteins. Our algorithm employs evolutionary search not to seek an optimum, but to seek surprises. It directs experiments and incrementally constructs an empirical model from their outcome. Reward is given for discovering conditions that exhibit a discrepancy between the prediction of the current model and the experimental result. As unexpected observations are incorporated into the model, the reward associated with them vanishes. Results obtained so far indicate that evolutionary search is a useful paradigm for characterizing the phenomenology of context-sensitive components.
Autonomous experimentation, scientific discovery, evolution
14-20
Pfaffmann, J. O.
704cf642-05e6-48bb-a1ae-22f05f1f2c16
Zauner, K.-P.
c8b22dbd-10e6-43d8-813b-0766f985cc97
2001
Pfaffmann, J. O.
704cf642-05e6-48bb-a1ae-22f05f1f2c16
Zauner, K.-P.
c8b22dbd-10e6-43d8-813b-0766f985cc97
Pfaffmann, J. O. and Zauner, K.-P.
(2001)
Scouting Context-sensitive Components.
In,
Keymeulen, D., Stoica, A., Lohn, J. and Zebulum, R. S.
(eds.)
The Third NASA/DoD Workshop on Evolvable Hardware---EH-2001.
IEEE, .
Record type:
Book Section
Abstract
Nature's gadgets are implemented without being planned and therefore can utilize context-sensitive components. Thus functionality that would require extensive networks of context-free components can be elicited from a minimum of material. Proteins can serve as context-sensitive components for pattern processing applications. We here describe an evolutionary search strategy currently under investigation for its potential use in conjunction with computer controlled fluidics to evaluate the computational capabilities of proteins. Our algorithm employs evolutionary search not to seek an optimum, but to seek surprises. It directs experiments and incrementally constructs an empirical model from their outcome. Reward is given for discovering conditions that exhibit a discrepancy between the prediction of the current model and the experimental result. As unexpected observations are incorporated into the model, the reward associated with them vanishes. Results obtained so far indicate that evolutionary search is a useful paradigm for characterizing the phenomenology of context-sensitive components.
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PfaffmannJO01Scouting.pdf
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More information
Published date: 2001
Keywords:
Autonomous experimentation, scientific discovery, evolution
Organisations:
Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 259135
URI: http://eprints.soton.ac.uk/id/eprint/259135
PURE UUID: 1a7975b0-9572-4417-b6f9-1a0711ff837c
Catalogue record
Date deposited: 12 Mar 2004
Last modified: 14 Mar 2024 06:20
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Contributors
Author:
J. O. Pfaffmann
Author:
K.-P. Zauner
Editor:
D. Keymeulen
Editor:
A. Stoica
Editor:
J. Lohn
Editor:
R. S. Zebulum
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