Talk:Evolutionary algorithm: Difference between revisions
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: Varying the mutation rate is not necessarily cheating but it is deviating from Richard Dawkins' purpose of demonstrating "random variation combined with non-random cumulative selection". The Weasel model uses a mutator and a selector. The mutator is intended to be random while the selector is non-random. If you add a non-random process to the mutator it breaks down the whole purpose of Dawkins' model. I don't understand why it's necessary to vary the mutation rate in the model. Is there biological evidence that nature reduces mutations when we near the ideal target? Dawkins states the notion of the ideal target is "absurd". It's important to stick with the purpose of the model and not change the essence of the model to simple converge more quickly. It's not a competition about who has the most rapidly converging model. --[[User:Davidj|Davidj]] 18:02, 1 October 2011 (UTC) |
: Varying the mutation rate is not necessarily cheating but it is deviating from Richard Dawkins' purpose of demonstrating "random variation combined with non-random cumulative selection". The Weasel model uses a mutator and a selector. The mutator is intended to be random while the selector is non-random. If you add a non-random process to the mutator it breaks down the whole purpose of Dawkins' model. I don't understand why it's necessary to vary the mutation rate in the model. Is there biological evidence that nature reduces mutations when we near the ideal target? Dawkins states the notion of the ideal target is "absurd". It's important to stick with the purpose of the model and not change the essence of the model to simple converge more quickly. It's not a competition about who has the most rapidly converging model. --[[User:Davidj|Davidj]] 18:02, 1 October 2011 (UTC) |
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: Out of curiosity, could the author of the C++ solution explain the constants 0.02 and 0.9 used to calculate the mutation_rate. Thank you. (double const mutation_rate = 0.02 + (0.9*fitness)/initial_fitness;) --[[User:Davidj|Davidj]] 18:02, 1 October 2011 (UTC) |
: Out of curiosity, could the author of the C++ solution explain the constants 0.02 and 0.9 used to calculate the mutation_rate. Thank you. (double const mutation_rate = 0.02 + (0.9*fitness)/initial_fitness;) --[[User:Davidj|Davidj]] 18:02, 1 October 2011 (UTC) |
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In answer to the first questioner of this section, the target is clearly given and is a static value. This is a major departure from what happens naturally. This is a task to show evolution, as in the gradual development of an answer towards a goal and shouldn't be taken as the answer to evolution theory sceptics. There is no intended cheating in the Python solution, it just "is what it is" and was written to follow the task goals.<br> |
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I wrote the task and the Python solution without being being an expert in evolutionary algorithms. It may be that what the knowledgable call an evolutionary algorithm has necessary aspects that are not part of this task description - if so, then I apologise; but I have tried to make the Python solution fit the task description as given, and did do some research into the subject at the time. --[[User:Paddy3118|Paddy3118]] 21:34, 1 October 2011 (UTC) |
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== "Official" Algorithm == |
== "Official" Algorithm == |
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