基于知识转移的分类器辅助进化算法

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Min Hu, Zhigang Ren, Zhirui Cao, Yifeng Guo, Haitao Sun, Hongyao Zhou, Yu Guo
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引用次数: 0

摘要

代理辅助进化算法为复杂且计算量大的优化问题提供了一种有效的手段。然而,由于训练样本的稀缺性,随着问题难度的增加,常用的回归代理模型的预测精度很难得到保证,导致整个算法的性能下降。由于现实世界中的问题很少是孤立存在的,因此期望通过适当地利用不同问题之间共享的知识来缓解上述问题。在此背景下,本研究提出了一种新的进化多任务优化算法,该算法由分类器辅助而不是回归模型来解决昂贵的多任务问题,其中分类器的准确性通过知识转移来提高。具体而言,首先开发了支持向量分类器(SVC),并将其集成到经典进化算法协方差矩阵自适应进化策略(CMA-ES)中。由于计算成本低,它有助于CMA-ES从当前种群中预先筛选母解决方案。然后,设计了一种知识转移策略,通过在不同任务之间共享高质量的解决方案来丰富每个面向任务的分类器的训练样本,其中采用了基于pca的子空间对齐技术。大量的实验表明,svc辅助的CMA-ES在鲁棒性和可扩展性方面都优于一般的CMA-ES,并且知识转移策略进一步帮助它在昂贵的多任务优化问题上比一些最先进的算法获得竞争优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A classifier-assisted evolutionary algorithm with knowledge transfer for expensive multitasking problems

Surrogate-assisted evolutionary algorithms provide an effective means for complex and computationally expensive optimization problems. However, due to the scarcity of training samples, the prediction accuracy of frequently-used regression surrogate models can hardly be guaranteed as the difficulty of the problem increases, resulting in performance degradation of the whole algorithm. Since real-world problems rarely exist in isolation, it is expected to alleviate the above issue by properly exploiting the knowledge shared across different problems. In this context, this study proposes a novel evolutionary multitasking optimization algorithm assisted by a classifier rather than a regression model for expensive multitasking problems, where the accuracy of the classifier is boosted by knowledge transfer. To be specific, a support vector classifier (SVC) is first developed and integrated into a classic evolutionary algorithm, i.e., covariance matrix adaptation evolution strategy (CMA-ES). With a low computational cost, it helps CMA-ES to prescreen parent solutions from the current population. Following that, a knowledge transfer strategy is designed to enrich the training samples for each task-oriented classifier by sharing high-quality solutions among different tasks, where a PCA-based subspace alignment technique is employed. Extensive experiments indicate that the SVC-assisted CMA-ES gains significant superiority over general CMA-ES in terms of both robustness and scalability, and the knowledge transfer strategy further helps it earn a competitive edge over some state-of-the-art algorithms on expensive multitasking optimization problems.

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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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