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The crossover rate, $p_c \in [0, 1]$, is a hyper-parameter that controls the rate at which solutions are subjected to crossover. So, the higher $p_c$, the more crossovers you perform, so the more diversity (in terms of solutions/chromosomes) you may introduce in the population. Typical values of $p_c$ are in the range $[0.5, 1.0]$. For example, in this ...

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In NEAT, the innovation of a node does not affect the evolution directly. Only the connection genes and their innovation will matter. So you can simply have whole numbers as IDs under each Genome / Network. --EDIT-- (Complete reasoning) In the original paper, it is clearly stated that the nodes from the better genome is taken during crossover and then only ...

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First of all, for a lot of realistic problems, the fitness function evaluation is usually orders of magnitude greater in complexity than the rest of the genetic algorithm. This is not always true, but often is true (e.g. imagine trying to optimise a simulation where you need to execute the simulation completely to obtain the fitness). So optimising the GA ...

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