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Team:Newcastle University/Evolutionary Algorithm
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{{:Team:Newcastle University/Header}} | {{:Team:Newcastle University/Header}} | ||
| - | {{:Team:Newcastle University/Template: | + | {{:Team:Newcastle University/Template:UnderTheApproach|page-title=[[Team:Newcastle University/Evolutionary Algorithm|Evolutionary Algorithm]]}} |
==Evolutionary Algorithm== | ==Evolutionary Algorithm== | ||
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* Reads parts list from [[Team:Newcastle University/Rarts Repository|parts repository]] | * Reads parts list from [[Team:Newcastle University/Rarts Repository|parts repository]] | ||
* Reads constraints on parts assembly (from [[Team:Newcastle University/Constraints Repository|Constraints Repository]]) | * Reads constraints on parts assembly (from [[Team:Newcastle University/Constraints Repository|Constraints Repository]]) | ||
| - | * Evolutionary algorithm assembles part models to a larger model | + | * Evolutionary algorithm (EA) assembles part models to a larger model |
* Simulates the behaviour of the composite model | * Simulates the behaviour of the composite model | ||
* Reads desired 'input' behaviour from [[Team:Newcastle University/Workbench|Workbench]] | * Reads desired 'input' behaviour from [[Team:Newcastle University/Workbench|Workbench]] | ||
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===Contributors:=== | ===Contributors:=== | ||
| - | Lead: [[Team:Newcastle University/Mark Wappett]] | + | Lead: [[Team:Newcastle University/Mark Wappett|Mark Wappett]] |
| + | |||
| + | ---- | ||
| + | ===Outcomes=== | ||
| + | The work for the EA was concluded on 1 Sept 2008. | ||
| + | |||
| + | ==== ==== | ||
Revision as of 14:36, 28 October 2008
Newcastle University
GOLD MEDAL WINNER 2008
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Home >> Original Aims >> Evolutionary Algorithm
Evolutionary Algorithm
Aim:
Develop a system that will evolve genetic circuits represented as networks that meet the functional requirements specified by the team's target application.
Traditional genetic engineering techniques have built small biological circuits by hand. However, this approach will not scale to whole-organism engineering. For synthetic biology at this scale computational design will be essential. “Soft” computing techniques such as evolutionary computation and computational intelligence were developed to handle exactly this sort of large, complex, hard-to-define problem.
Objectives:
- Reads parts list from parts repository
- Reads constraints on parts assembly (from Constraints Repository)
- Evolutionary algorithm (EA) assembles part models to a larger model
- Simulates the behaviour of the composite model
- Reads desired 'input' behaviour from Workbench
- Reads desired 'output' behaviour from workbench
- Assesses fitness
- Mutates the model
- Output the fittest model as CellML (to workbench)
Contributors:
Lead: Mark Wappett
Outcomes
The work for the EA was concluded on 1 Sept 2008.
