Paper Info
Title
A Quasi-Parallel Realization of the Investment Frontier in Computer Resource Allocation Using Simple Genetic Algorithm on a Single Computer
Abstract
The concept of portfolio management of algorithm is implemented in a new architecture based on ideas of cooperating multi-agents. Each agent is a simple genetic algorithm with identical structure but possibly different parameters. We introduce a "resource allocation vector" to coordinate the computing resources allocated to each agent. We also encourage constructive collaboration among agents by the exchange of the individuals in the population of each genetic algorithm using an individual-migration matrix. The algorithm can be implemented in a serial computer and behaves statistically in a quasi-parallel manner. We have performed extensive statistical analysis using measures such as the mean and variance of the first passage time to solution. The existence of investment frontier in solving the Schaffer function problem is demonstrated and application to the solving of the traveling salesman problem is discussed. The results suggest a more effective way to utilize computing resources.
Year
Venue
Keywords
2002
PARA
simple genetic algorithm,investment frontier,genetic algorithm,computer resource allocation,salesman problem,quasi-parallel realization,different parameter,schaffer function problem,individual-migration matrix,computing resource,constructive collaboration,single computer,extensive statistical analysis,identical structure,statistical analysis,resource allocation,first passage time,portfolio management,utility computing,traveling salesman problem
Field
DocType
Volume
Population,Autonomous agent,Computer science,Theoretical computer science,Travelling salesman problem,Resource allocation,Function problem,Serial computer,Population-based incremental learning,Genetic algorithm
Conference
2367
ISSN
ISBN
Citations 
0302-9743
3-540-43786-X
5
PageRank 
References 
Authors
1.21
7
2
Name
Order
Citations
PageRank
Kwok Yip Szeto16421.47
Rui Jiang233040.72