Evolving Agent-based Models Using Complexification Approach

被引：0

作者：

Wagner, Michael ^{[1
]}

Cai, Wentong ^{[2
]}

Lees, Michael H. ^{[3
]}

Aydt, Heiko ^{[1
]}

机构：

[1] TUM CREATE, 1 Create Way, Singapore 138602, Singapore

[2] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore

[3] Univ Amsterdam, Inst Informat, Amsterdam, Netherlands

来源：

2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE | 2014年 / 29卷

基金：

新加坡国家研究基金会;

关键词：

Evolutionary Computing; Agent-based Simulation; Modelling; Artificial Life;

D O I：

10.1016/j.procs.2014.05.028

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper focuses on parameter search for multi-agent based models using evolutionary algorithms. Large numbers and variable dimensions of parameters require a search method which can efficiently handle a high dimensional search space. We are proposing the use of complexification as it emulates the natural way of evolution by starting with a small constrained search space and expanding it as the evolution progresses. We examined the effects of this method on an EA by evolving parameters for two multi-agent based models.

引用

页码：310 / 321

页数：12

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