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1

It's not obvious what you mean by "intelligent crossover". However, it is common to use fitness-based selection of parents: individuals in the current population who have higher fitness are assigned a higher probability of being selected to mate and produce offspring. This will increase the likelihood that "good" combinations of genes in members of the ...


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There are multiple ways to handle 'illegal' individuals, each one with pros and cons: Abortive methods: The individuals that violate constraints are eliminated as soon as discovered (i.e. after crossover or mutation) and new individuals are generated in order to keep the population stable. This usually implies a slower creation of new generations, as ...


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Yes it has been tried. In fact there is a whole field, dubbed Genetic Programming. There is an annual competition to obtain "Human-Competitive" algorithms, and many instances of those have been found over the years.


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I imagine that using the MaxNFFC as stop criteria only happens in very particular implementations. And this is its main disadvantage. Normally you'd evaluate each individual, each generation. So NFFC will always be the same as the size of the population times the number of epochs (+1 to consider the initial population). $ N*(E+1) $ As population ...


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If I understood correctly, your problem is about finding the optimal way to execute a series of tasks in order to maximize the results, using Genetic Algorithms. In few words, you're trying to solve the salesman problem. If I am correct, you're looking for Crossover and Mutation algorithms that allow you to work with ordered sets of elements. For these ...


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Not really. Unity physics is just an approximation of an approximation. It has to look more or less real but at the same time the performances are very important, so it has not the realistic level you would hope to "bring things to the real world". There are some physics engine you can install that usually work a bit better. Still, don't expect "real-world ...


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I think that the best approach is to "switch point of view" from the general, objective-oriented, Genetic Algorithm's behaviours. Usually GAs rely on individualism: the best survives. To do this you have to define what 'best' means and this is done through a fitness function that measures something objective, independent from the individual (i.e. a score, ...


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