个人对进化计算的看法:35年的历程

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_00323
Zbigniew Michalewicz
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引用次数: 2

摘要

本文介绍了作者在进化计算领域35年的个人经历——从1988年第一次接触到多年的学术研究,一直到在商业领域全职工作——成功地为一些世界上最大的公司实现了进化算法。文章最后提出了一些观察和见解。
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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A Personal Perspective on Evolutionary Computation: A 35-Year Journey
This paper presents a personal account of the author's 35 years “adventure” with Evolutionary Computation—from the first encounter in 1988 and many years of academic research through to working full-time in business—successfully implementing evolutionary algorithms for some of the world's largest corporations. The paper concludes with some observations and insights.
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
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.
期刊最新文献
Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm. Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms. Genetic Programming for Automatically Evolving Multiple Features to Classification. A Tri-Objective Method for Bi-Objective Feature Selection in Classification. Preliminary Analysis of Simple Novelty Search.
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