4

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 ...


3

The "objective function" is the function that you want to minimise or maximise in your problem. The expression "objective function" is used in several different contexts (e.g. machine learning or linear programming), but it always refers to the function to be maximised or minimised in the specific (optimisation) problem. Hence, this expression is used in ...


2

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 ...


2

I wrote some python code to reproduce this paper's purported results. My code very efficiently optimizes simple smooth functions like bowls, but does not come close to reproducing the paper's claimed results on more complex functions, including with the parameters the authors report. I think that, since both @Jairo and I were unable to reproduce the results ...


2

How to find the best configuration for an algorithm is an open research question in AI. The topic in general is known as `hyper-parameter optimization' and there are a range of possible methods: One of the most popular is IRace, but other possibilities include: Spearmint: uses wrappers in Matlab or Python. It uses MongoDb, and Bayesian optimisation ...


1

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, ...


1

Meta-heuristics are particularly suited for combinatorial optimization problems, given that, although they are not usually guaranteed to find the optimal global solution, they can often find a sufficiently good solution in a decent amount of time. So, they are an alternative to exhaustive search, which would take exponential time. For example, ant colony ...


Only top voted, non community-wiki answers of a minimum length are eligible