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Team:Groningen/modeling genetic algorithms
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==How genetic algorithms work== | ==How genetic algorithms work== | ||
| - | A genetic alogoritm mimics the process | + | A genetic alogoritm mimics the process of population genetics in order to optimise some fitness criteron. In our case this criterion is based on how good the simulated data matches the experimental results. By |
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===Mutation=== | ===Mutation=== | ||
| + | In the mutation step we add new exampk | ||
* crossing over | * crossing over | ||
===Evaluation=== | ===Evaluation=== | ||
| + | In this step yet unevaluated individuals are evaluated | ||
| + | |||
===Selection=== | ===Selection=== | ||
| + | In the selection step we discard some individuals of our population that we deem not good enough | ||
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{{FooterGroningen2011}} | {{FooterGroningen2011}} | ||
Revision as of 09:13, 31 August 2011
Genetic algorithms
Genetic algorithms, (also reffered to as dynamic programming or
Convergence
How genetic algorithms work
A genetic alogoritm mimics the process of population genetics in order to optimise some fitness criteron. In our case this criterion is based on how good the simulated data matches the experimental results. By
It repeats teh following step a number of times (either a fixed number of times, or contrained by some metrix such as the fitness or the change thereof.)
Mutation
In the mutation step we add new exampk
- crossing over
Evaluation
In this step yet unevaluated individuals are evaluated
Selection
In the selection step we discard some individuals of our population that we deem not good enough
