Tuning Metaheuristics
Birattari, Mauro.
Tuning Metaheuristics A Machine Learning Perspective / [electronic resource] : by Mauro Birattari. - X, 221 p. online resource. - Studies in Computational Intelligence, 197 1860-949X ; . - Studies in Computational Intelligence, 197 .
Background and State-of-the-Art -- Statement of the Tuning Problem -- F-Race for Tuning Metaheuristics -- Experiments and Applications -- Some Considerations on the Experimental Methodology -- Conclusions.
The importance of tuning metaheuristics is widely acknowledged in scientific literature. However, there is very little dedicated research on the subject. Typically, scientists and practitioners tune metaheuristics by hand, guided only by their experience and by some rules of thumb. Tuning metaheuristics is often considered to be more of an art than a science. This book lays the foundations for a scientific approach to tuning metaheuristics. The fundamental intuition that underlies Birattari's approach is that the tuning problem has much in common with the problems that are typically faced in machine learning. By adopting a machine learning perspective, the author gives a formal definition of the tuning problem, develops a generic algorithm for tuning metaheuristics, and defines an appropriate experimental methodology for assessing the performance of metaheuristics.
9783642004834
10.1007/978-3-642-00483-4 doi
Engineering.
Artificial intelligence.
Engineering mathematics.
Engineering.
Appl.Mathematics/Computational Methods of Engineering.
Artificial Intelligence (incl. Robotics).
TA329-348 TA640-643
519
Tuning Metaheuristics A Machine Learning Perspective / [electronic resource] : by Mauro Birattari. - X, 221 p. online resource. - Studies in Computational Intelligence, 197 1860-949X ; . - Studies in Computational Intelligence, 197 .
Background and State-of-the-Art -- Statement of the Tuning Problem -- F-Race for Tuning Metaheuristics -- Experiments and Applications -- Some Considerations on the Experimental Methodology -- Conclusions.
The importance of tuning metaheuristics is widely acknowledged in scientific literature. However, there is very little dedicated research on the subject. Typically, scientists and practitioners tune metaheuristics by hand, guided only by their experience and by some rules of thumb. Tuning metaheuristics is often considered to be more of an art than a science. This book lays the foundations for a scientific approach to tuning metaheuristics. The fundamental intuition that underlies Birattari's approach is that the tuning problem has much in common with the problems that are typically faced in machine learning. By adopting a machine learning perspective, the author gives a formal definition of the tuning problem, develops a generic algorithm for tuning metaheuristics, and defines an appropriate experimental methodology for assessing the performance of metaheuristics.
9783642004834
10.1007/978-3-642-00483-4 doi
Engineering.
Artificial intelligence.
Engineering mathematics.
Engineering.
Appl.Mathematics/Computational Methods of Engineering.
Artificial Intelligence (incl. Robotics).
TA329-348 TA640-643
519