Population aggregation based on fitness

Authors:
Cheah C. J. Moey;Jonathan E. Rowe
Affiliations:
School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK (E-mails: ccm@cs.bham.ac.uk);School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK (E-mails: jer@cs.bham.ac.uk)
Venue:
Natural Computing: an international journal
Year:
2004

Citing 8
Cited 5

Finite Markov chain analysis of genetic algorithms

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Statistical dynamics of the Royal Road genetic algorithm

Theoretical Computer Science - Special issue on evolutionary computation
Modelling genetic algorithm dynamics

Theoretical aspects of evolutionary computing
Statistical mechanics theory of genetic algorithms

Theoretical aspects of evolutionary computing
The Simple Genetic Algorithm: Foundations and Theory

The Simple Genetic Algorithm: Foundations and Theory
Finite Markov Chain Analysis of Genetic Algorithms with Niching

Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory

Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Schemata evolution and building blocks

Evolutionary Computation

Aggregability is NP-hard

ACM SIGACT News
Quotients of Markov chains and asymptotic properties of the stationary distribution of the Markov chain associated to an evolutionary algorithm

Genetic Programming and Evolvable Machines
The crowding approach to niching in genetic algorithms

Evolutionary Computation
Estimating the ratios of the stationary distribution values for markov chains modeling evolutionary algorithms

Evolutionary Computation
A Markov model for headway/spacing distribution of road traffic

IEEE Transactions on Intelligent Transportation Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is known that modelling a finite population genetic algorithmas a Markov chain requires a prohibitively large number of states.In an attempt to resolve this problem, a number of state aggregationtechniques have been proposed. We consider two different strategies for aggregating populations, one using equal average fitness and theother using equal best fitness. We examine how the approximation scales with population size, in addition to studying the effects of other parameters (such as mutation rate). We find that a large reduction in the number of states is possible, sometimes with surprisingly small loss of accuracy.