基于自适应遗传算法的竞价策略演化

G. K. Soon, P. Anthony, J. Teo, C. K. On
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引用次数: 5

摘要

本文研究了自适应遗传算法在灵活且可配置的启发式决策框架上的应用,该框架可以通过使用自主代理搜索最有效的策略(离线)来解决应用不同协议(英语,Vickrey和Dutch)的多个拍卖的竞标问题。研究表明,自适应遗传算法的性能明显优于传统的遗传算法。本文对启发式决策框架下遗传算法和自适应遗传算法搜索最有效策略的有效性进行了实证评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolving Bidding Strategies Using Self-Adaptation Genetic Algorithm
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.
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