对囚徒困境演化策略早期研究的个人思考

IF 4.6 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Evolutionary Computation Pub Date : 2023-06-01 DOI:10.1162/evco_a_00322

David B. Fogel

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引用次数: 1

摘要

在《进化计算》杂志创刊30周年之际，Hart教授邀请我就我在1993年创刊的那篇关于反复囚徒困境中的进化行为的文章发表一些感想。我很荣幸能这样做。我要感谢该杂志的首任主编Ken De Jong教授，感谢他在创办该杂志时的远见卓识，以及追随并保持这一远见卓识的编辑们。这篇文章包含了对这个话题和整个领域的一些个人思考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Personal Reflections on Some Early Work in Evolving Strategies in the Iterated Prisoner's Dilemma

On the occasion of the 30-year anniversary of the Evolutionary Computation journal, I was invited by Professor Hart to offer some reflections on the article on evolving behaviors in the iterated prisoner's dilemma that I contributed to its first issue in 1993. It's an honor to do so. I would like to thank Professor Ken De Jong, the journal's first editor-in-chief, for his vision in creating the journal, and the editors who have followed and maintained that vision. This article contains some personal reflections on the topic and the field as a whole.

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来源期刊

Evolutionary Computation 工程技术-计算机：理论方法

CiteScore

6.40

自引率

1.50%

发文量

审稿时长

3 months

期刊介绍： Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.