Essentials of Metaheuristics, second edition (bibtex)
by Sean Luke
Abstract:
Metaheuristics is a rather unfortunate1 term often used to describe a major subfield, indeed the primary subfield, of stochastic optimization. Stochastic optimization is the general class of algorithms and techniques which employ some degree of randomness to find optimal (or as optimal as possible) solutions to hard problems. Metaheuristics are the most general of these kinds of algorithms, and are applied to a very wide range of problems. What kinds of problems? In Jacobellis v. Ohio (1964, regarding obscenity), the United States Supreme Court, Justice Potter Stewart famously wrote, I shall not today attempt further to define the kinds of material I understand to be embraced within that shorthand description; and perhaps I could never succeed in intelligibly doing so. But I know it when I see it, and the motion picture involved in this case is not that. Metaheuristics are applied to I know it when I see it problems. They’re algorithms used to find answers to problems when you have very little to help you: you don’t know what the optimal solution looks like, you don’t know how to go about finding it in a principled way, you have very little heuristic information to go on, and brute-force search is out of the question because the space is too large. But if you’re given a candidate solution to your problem, you can test it and assess how good it is. That is, you know a good one when you see it. For example: imagine if you’re trying to find an optimal set of robot behaviors for a soccer goalie robot. You have a simulator for the robot and can test any given robot behavior set and assign it a quality (you know a good one when you see it). And you’ve come up with a definition for what robot behavior sets look like in general. But you have no idea what the optimal behavior set is, nor even how to go about finding it.
Reference:
Essentials of Metaheuristics, second edition (Sean Luke), (Sean Luke, ed.), lulu.com, 2013.
Bibtex Entry:
@BOOK{Luke2013,
  ABSTRACT  = {Metaheuristics is a rather unfortunate1 term often used to describe a major subfield, indeed the primary subfield, of stochastic optimization. Stochastic optimization is the general class of algorithms and techniques which employ some degree of randomness to find optimal (or as optimal as possible) solutions to hard problems. Metaheuristics are the most general of these kinds of algorithms, and are applied to a very wide range of problems. What kinds of problems? In Jacobellis v. Ohio (1964, regarding obscenity), the United States Supreme Court, Justice Potter Stewart famously wrote, I shall not today attempt further to define the kinds of material I understand to be embraced within that shorthand description; and perhaps I could never succeed in intelligibly doing so. But I know it when I see it, and the motion picture involved in this case is not that. Metaheuristics are applied to I know it when I see it problems. They’re algorithms used to find answers to problems when you have very little to help you: you don’t know what the optimal solution looks like, you don’t know how to go about finding it in a principled way, you have very little heuristic information to go on, and brute-force search is out of the question because the space is too large. But if you’re given a candidate solution to your problem, you can test it and assess how good it is. That is, you know a good one when you see it. For example: imagine if you’re trying to find an optimal set of robot behaviors for a soccer goalie robot. You have a simulator for the robot and can test any given robot behavior set and assign it a quality (you know a good one when you see it). And you’ve come up with a definition for what robot behavior sets look like in general. But you have no idea what the optimal behavior set is, nor even how to go about finding it.},
  AUTHOR    = {Luke, Sean},
  BOOKTITLE = {Optimization},
  EDITOR    = {Luke, Sean},
  FILE      = {http://bib.unthinkingdepths.fr/seb/pdf//Luke_2013_Essentials of Metaheuristics, second edition.pdf},
  ISBN      = {9781300549628},
  PAGES     = {1--235},
  PUBLISHER = {lulu.com},
  TITLE     = {{Essentials of Metaheuristics, second edition}},
  URL       = {http://cs.gmu.edu/$backslash sim$sean/book/metaheuristics/ http://cs.gmu.edu/~sean/book/metaheuristics/},
  YEAR      = {2013},
}
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