8 votes
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What are hyper-heuristics, and how are they different from meta-heuristics?

TL:DR: Hyper-heuristics are metaheuristics, suited for solving the same kind of optimization problems, but (in principle) affording a "rapid prototyping" approach for non-expert practitioners. In ...
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4 votes
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What is the difference between Stochastic Hill Climbing and Simulated Annealing?

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 ...
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4 votes
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What is an objective function?

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 ...
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2 votes
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Does a differential evolution algorithm mutate its population during a generation?

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 ...
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2 votes

Why can't I reproduce the experiments in the original paper that introduced the Firefly Algorithm?

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 ...
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2 votes

How to find proper parameter settings for a given optimization algorithm?

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 ...
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1 vote

Why does Simulated Annealing not take worse solution if the energy difference becomes higher?

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 ...
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1 vote

What are advantages of using meta-heuristic algorithms on optimization problems?

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 ...
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