Problem solving using cultural algorithms

Author

Reynolds, Robert G. ; Sverdlik, William

Author_Institution

Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA

fYear

1994

fDate

27-29 Jun 1994

Firstpage

645

Abstract

In this paper an approach to evolutionary learning based upon principles of cultural evolution is developed. In this dual-inheritance system, there is an evolving population of trait sequences as well as an associated belief space. The belief space is derived from the behavior of individuals and is used to actively constrain the traits acquired in future populations. Shifts in the representation of the belief space and the population is supported. The approach is used to solve several versions of the BOOLE problem; F6, F11, and F20. The results are compared with other approaches and the advantages of a dual inheritance approach using cultural algorithms is discussed

Keywords

genetic algorithms; inheritance; learning (artificial intelligence); problem solving; BOOLE problem; belief space; cultural algorithms; dual-inheritance system; evolutionary learning; inheritance; trait sequences; Agriculture; Computer science; Context modeling; Cultural differences; Frequency; Genetic algorithms; Humans; Problem-solving; Space technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1899-4

Type

conf

DOI

10.1109/ICEC.1994.349983

Filename

349983