Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning

Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning (1994)

by Shumeet Baluja

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Datum	Value	Source
TITLE	Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning	INFERENCE
AUTHOR NAME	Shumeet Baluja	SVM HeaderParse 0.2
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR ADDR	Pittsburgh, Pennsylvania 15213	SVM HeaderParse 0.2
ABSTRACT	with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores population -based incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning. The combination of these two methods reveals a tool which is far simpler than a GA, and which out-performs a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform better.	user correction - Legacy Corrections
YEAR	1994	SVM HeaderParse 0.2
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical Report CMU-CS-94-163	INFERENCE
CITATIONS	56 found	ParsCit 1.0