Using fitness distributions to improve the evolution of learning structures

Author

Igel, Christian ; Kreutz, Martin

Author_Institution

Inst. fur Neuroinf., Ruhr-Univ., Bochum, Germany

Volume

3

fYear

1999

fDate

1999

Abstract

The absolute benefit, a measure of improvement in the fitness space, is derived from the viewpoint of fitness distribution and fitness trajectory analysis. It is used for online operator adaptation, where the optimization of density estimation models serves as an example. A new information theory based measure is proposed to judge the accuracy of the evolved models. Further, the absolute benefit is applied to offline analysis of new gradient based operators used for coefficient adaptation in genetic programming. An efficient method to calculate the gradient information is presented

Keywords

genetic algorithms; information theory; learning (artificial intelligence); probability; absolute benefit; coefficient adaptation; density estimation models; fitness distributions; fitness space; fitness trajectory analysis; genetic programming; gradient based operators; gradient information; information theory based measure; learning structure evolution; offline analysis; online operator adaptation; Algorithm design and analysis; Density measurement; Distributed computing; Evolutionary computation; Gain measurement; Genetic communication; Genetic programming; Information theory;

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.1999.785505

Filename

785505