超越景观分析:DynamoRep功能用于捕捉算法-问题交互在单一目标的持续优化
Gjorgjina Cenikj1,2, Gašper Petelin1,3, Carola Doerr4
1Computer Systems Department, Jožef Stefan Institute, Ljubljana, 1000, Slovenia.
DynamoRep 功能提供了一种具有成本效益的方法来表示算法-问题交互. 这些特性可以与高级方法实现可比的结果,用于问题分类和优化中的算法选择.
科学领域:
- 计算智能是一种计算智能.
- 数字优化优化 数字优化
- 机器学习用于优化优化.
背景情况:
- 数字特征对于分析优化问题和算法至关重要.
- 现有的方法往往只关注问题特征.
- DynamoRep的功能可以有效地捕捉算法与问题之间的交互.
研究的目的:
- 为了全面分析DynamoRep功能的预测能力.
- 为了评估DynamoRep的问题分类,算法选择和算法分类.
- 为了比较DynamoRep的性能与最先进的功能.
主要方法:
- 利用了DynamoRep的功能,用于算法-问题交互表示.
- 在BBOB基准套件中应用了问题分类的特征.
- 从一个投资组合中评估算法选择 (差异进化,进化策略,粒子优化).
- 基于优化轨迹的评估算法分类.
主要成果:
- DynamoRep 功能在任务中表现出强大的预测能力.
- 性能与计算上昂贵的探索性景观分析功能相当.
- 有效地对BBOB问题实例进行分类.
- 在选择最佳算法和区分算法行为方面取得成功.
结论:
- DynamoRep提供了一个计算上便宜但功能强大的功能表示.
- 这些功能对于优化中的各种机器学习任务非常有效.
- DynamoRep是对现有的,更复杂的功能集的有希望的替代品.
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