Adaptive genetic algorithms-modeling and convergence

Author

Agapie, Alexandru

Author_Institution

Comput. Intelligence Lab., Inst. of Microtechnol., Bucharest, Romania

Volume

1

fYear

1999

fDate

1999

Abstract

The paper presents a new mathematical analysis of genetic algorithms (GAs); we propose the use of random systems with complete connections (RSCC), a non-trivial extension of the Markovian dependence, accounting for a complete, rather than recent, history of a stochastic evolution. As far as we know, this is the first theoretical modeling of an adaptive GA. First we introduce the RSCC model of an p_m-adaptive GA, then we prove that a “classification of states” is still valid for our model, and finally we derive a convergence condition for the algorithm

Keywords

Markov processes; algorithm theory; convergence of numerical methods; genetic algorithms; Markov chain; Markovian dependence; RSCC model; adaptive genetic algorithms; classification of states; complete connections; convergence condition; mathematical analysis; p_m-adaptive GA; random systems; stochastic evolution; Algorithm design and analysis; Combinatorial mathematics; Computational intelligence; Computational modeling; Convergence; Genetic algorithms; Genetic mutations; History; Stochastic systems; Sufficient conditions;

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.1999.782005

Filename

782005