Predictive measures for problem difficulty

Author

Reeves, Colin R.

Author_Institution

Sch. of Math. & Inf. Sci., Coventry Univ., UK

Volume

1

fYear

1999

fDate

1999

Abstract

In practical applications to instances of optimization problems it would be of great benefit if we could decide a priori which algorithm is suited to the problem; or at least, which of several candidates might be better than others. In some cases it is obvious: optimizing a linear function of variables subject to linear constraints for instance. However, such examples are rare, and this is certainly the case for potential applications of evolutionary algorithms (EAs). For this reason, various predictive measures have been suggested. These all sample the Universe of all potential solutions in order to compute some statistics that are thought to aid the decision. This paper describes some of these ideas and then discusses and evaluates them using an adversary argument

Keywords

algorithm theory; computability; genetic algorithms; epistasis; evolutionary algorithms; genetic algorithms; optimization problems; predictive measures; problem difficulty; Biological cells; Constraint optimization; Design for experiments; Evolutionary computation; Genetic algorithms; History; Prediction algorithms; Search methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on

Conference_Location

Washington, DC

Print_ISBN

0-7803-5536-9

Type

conf

DOI

10.1109/CEC.1999.782006

Filename

782006