The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations

被引：181

作者：

Harik, George ^{[1
]}

Cantu-Paz, Erick ^{[1
]}

Goldberg, David E. ^{[1
]}

Miller, Brad L. ^{[2
]}

机构：

[1] Univ Illinois, Illinois Genet Algorithms Lab, Urbana, IL 61801 USA

[2] I2 Technol, Cambridge, MA 02139 USA

来源：

EVOLUTIONARY COMPUTATION | 1999年 / 7卷 / 03期

关键词：

Population size; noise; decision making; building block supply;

D O I：

10.1162/evco.1999.7.3.231

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a model to predict the convergence quality of genetic algorithms based on the size of the population. The model is based on an analogy between selection in GAS and one-dimensional random walks. Using the solution to a classic random walk problem-the gambler's ruin-the model naturally incorporates previous knowledge about the initial supply of building blocks (BBs) and correct selection of the best BB over its competitors. The result is an equation that relates the size of the population with the desired quality of the solution, as well as the problem size and difficulty. The accuracy of the model is verified with experiments using additively decomposable functions of varying difficulty. The paper demonstrates how to adjust the model to account for noise present in the fitness evaluation and for different tournament sizes.

引用

页码：231 / 253

页数：23

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