Publication Details
Title: Fuzzy Evolutionary Algorithms
Author: H.-M. Voigt
Group: ICSI Technical Reports
Date: June 1992
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1992/tr-92-038.pdf
Overview:
Evolutionary algorithms (EA) combine different approaches for solving complex problems based on principles, models, and mechanisms of natural evolution. Typical representatives of such algorithms are Genetic Algorithms (GA) and Evolution Strategies (ES), which are closely related in principle but show different emphasis on the representational and operational level. The basic ideas and concepts for GAs and ESs dates back to the early sixties. Central concepts of these approaches include the replication, recombination, mutation, selection, isolation-migration, and diffusion of individuals within or between populations or subpopulations, respectively. These algorithms do not take into account the development of an individual or organism from the gene level to the mature phenotype level. This development is a multistage decision process influenced by the environment and by interspecific as well as intraspecific competition and cooperation such that usually no inferences can be drawn from phenotype to genotype. The goal of this paper is to introduce a fuzzy representation and fuzzy operations to model the developmental process based on fuzzy decisions. Some first conclusions with respect to optimization will be stated. The appendices include an up-to-date software survey for Evolutionary Algorithms and the description of "The Evolution Machine."
Bibliographic Information:
ICSI Technical Report TR-92-038
Bibliographic Reference:
H.-M. Voigt. Fuzzy Evolutionary Algorithms. ICSI Technical Report TR-92-038, June 1992
Author: H.-M. Voigt
Group: ICSI Technical Reports
Date: June 1992
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1992/tr-92-038.pdf
Overview:
Evolutionary algorithms (EA) combine different approaches for solving complex problems based on principles, models, and mechanisms of natural evolution. Typical representatives of such algorithms are Genetic Algorithms (GA) and Evolution Strategies (ES), which are closely related in principle but show different emphasis on the representational and operational level. The basic ideas and concepts for GAs and ESs dates back to the early sixties. Central concepts of these approaches include the replication, recombination, mutation, selection, isolation-migration, and diffusion of individuals within or between populations or subpopulations, respectively. These algorithms do not take into account the development of an individual or organism from the gene level to the mature phenotype level. This development is a multistage decision process influenced by the environment and by interspecific as well as intraspecific competition and cooperation such that usually no inferences can be drawn from phenotype to genotype. The goal of this paper is to introduce a fuzzy representation and fuzzy operations to model the developmental process based on fuzzy decisions. Some first conclusions with respect to optimization will be stated. The appendices include an up-to-date software survey for Evolutionary Algorithms and the description of "The Evolution Machine."
Bibliographic Information:
ICSI Technical Report TR-92-038
Bibliographic Reference:
H.-M. Voigt. Fuzzy Evolutionary Algorithms. ICSI Technical Report TR-92-038, June 1992