Finite population models of dynamic optimization with stochastically alternating fitness functions

A.M.L. Liekens; H.M.M. Eikelder, ten; P.A.J. Hilbers

Finite population models of dynamic optimization with stochastically alternating fitness functions

A.M.L. Liekens, H.M.M. Eikelder, ten, P.A.J. Hilbers

Mathematics and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

We present a stochastic, finite population model of genetic algorithms in dynamic environments. In this model, fitness functions alternate stochastically over time. The limit behavior of these systems can beutilized to express predictions of expected behavior and measurements of performance for the algorithm and its parameter choices. We provide methods to analyze and study the limit behavior and performance measures for these systems. We also show how the model and its predictions relate to a previously studied model with deterministically alternating fitness functions.

Original language	English
Title of host publication	2003 Congress of Evolutionary Computation (CEC 2003), Australia, Canberra
Publication status	Published - 2003

Cite this

@inproceedings{39620663fa2c4e92876472820ae99821,

title = "Finite population models of dynamic optimization with stochastically alternating fitness functions",

abstract = "We present a stochastic, finite population model of genetic algorithms in dynamic environments. In this model, fitness functions alternate stochastically over time. The limit behavior of these systems can beutilized to express predictions of expected behavior and measurements of performance for the algorithm and its parameter choices. We provide methods to analyze and study the limit behavior and performance measures for these systems. We also show how the model and its predictions relate to a previously studied model with deterministically alternating fitness functions.",

author = "A.M.L. Liekens and {Eikelder, ten}, H.M.M. and P.A.J. Hilbers",

year = "2003",

language = "English",

booktitle = "2003 Congress of Evolutionary Computation (CEC 2003), Australia, Canberra",

}

TY - GEN

T1 - Finite population models of dynamic optimization with stochastically alternating fitness functions

AU - Liekens, A.M.L.

AU - Eikelder, ten, H.M.M.

AU - Hilbers, P.A.J.

PY - 2003

Y1 - 2003

N2 - We present a stochastic, finite population model of genetic algorithms in dynamic environments. In this model, fitness functions alternate stochastically over time. The limit behavior of these systems can beutilized to express predictions of expected behavior and measurements of performance for the algorithm and its parameter choices. We provide methods to analyze and study the limit behavior and performance measures for these systems. We also show how the model and its predictions relate to a previously studied model with deterministically alternating fitness functions.

AB - We present a stochastic, finite population model of genetic algorithms in dynamic environments. In this model, fitness functions alternate stochastically over time. The limit behavior of these systems can beutilized to express predictions of expected behavior and measurements of performance for the algorithm and its parameter choices. We provide methods to analyze and study the limit behavior and performance measures for these systems. We also show how the model and its predictions relate to a previously studied model with deterministically alternating fitness functions.

M3 - Conference contribution

BT - 2003 Congress of Evolutionary Computation (CEC 2003), Australia, Canberra

ER -

Finite population models of dynamic optimization with stochastically alternating fitness functions

Abstract

Fingerprint

Cite this