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Changelog
Giliard Almeida de Godoi edited this page Feb 15, 2021
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- Add a crossover probability for binary representation!
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Pass a function to calculate the individuals' weight (like a normalization function)Normalization as an Evolution Step. -
Refact evaluation method for binary: normalize population and update best inside evaluation;Refactoring binary chromosome evaluation; -
In the selection step, instead of returning a selected population, we could return a generator function. And the selected individual would be known in the crossover step. -
Considering the last item, we could use theoffspring_generatorin the crossover method. - Persist best solution found
- Edge set representation. Starting a new branch here!
- Generate individual like PrimRST (EdgeSet)
- Generate individual like KruskalRST (EdgeSet)
- Generate individual like RandomWalkRST (EdgeSet)
- Crossover PrimRST based (EdgeSet)
- Crossover KruskalRST based (EdgeSet)
- Crossover RandomWalkRST (EdgeSet)
- Edgeset strategies for Mutation: (EdgeSet)
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Insert an edge and remove anotherI don't think it's will be necessary. - Remove an edge and reconnect components
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- Persist best solution found.
- Implement mutation operator based on:
- Random descent
- Random jump