Evolving Bidding Strategies Using Self-Adaptation Genetic Algorithm

被引：1

作者：

Soon, Kim Gan ^{[1
]}

Patricia, Anthony ^{[1
]}

Jason, Teo ^{[1
]}

On, Kim Chin ^{[1
]}

机构：

[1] Univ Malaysia Sabah, Sch Engn & Informat Technol, Kota Kinabalu, Sabah, Malaysia

来源：

2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT UBIQUITOUS COMPUTING AND EDUCATION | 2009年

关键词：

Online Auction; Bidding Strategies; Bidding Agent; Genetic Algorithm; Self-Adaptation;

D O I：

10.1109/IUCE.2009.108

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 investigates the application of self-adaptation genetic algorithm on a flexible and configurable heuristic decision making framework that can tackle the problem of bidding across multiple auctions that apply different protocols (English, Vickrey and Dutch) by using an autonomous agent to search for the most effective strategies (offline). Our study shows that self-adaptation genetic algorithm performance is much better than conventional genetic algorithm. An empirical evaluation on the effectiveness of genetic algorithm and self-adaptation genetic algorithm for searching the most effective strategies in the heuristic decision making framework are discussed in this paper.

引用

页码：222 / 225

页数：4

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