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Russell and Norvig's book (3rd edition) describe these two algorithms (section 4.1.1., p. 122) and this book is the reference that you should generally use when studying search algorithms in artificial intelligence. I am familiar with simulated annealing (SA), given that I implemented it in the past to solve a combinatorial problem, but I am not very ...


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Quoting the original paper: For each target vector $x_{i,G}$ ,a mutant vector is generated according to $$ v_{i,G+1} = x_{r_1,G} + F\left(x_{r_2,G} + x_{r_3,G}\right)$$ And later To decide whether or not it should become a member of generation $G + 1$, the trial vector $v_{i,G+1}$ is compared to the target vector $x_{i,G}$ using the greedy criterion. I'd ...


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Note that you can't really predict whether your escape from a local minimum will work or not - you might just wind up in another, worse local minimum. The probability function you describe increases the likelihood of this happening. By upweighting the likelihood of allowing small energy differences, you allow for the possibility of escaping local minima, ...


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