Few-Shots Parallel Algorithm Portfolio Construction via Co-Evolution

Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to learn a model from vast data. In the context of heuristic search, such...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 25; H. 3; S. 595 - 607
Hauptverfasser:	Tang, Ke, Liu, Shengcai, Yang, Peng, Yao, Xin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm configuration automatic parameter tuning co-evolution Evolutionary algorithms Machine learning Machine learning algorithms parallel algorithm portfolios (PAPs) Parallel algorithms Portfolios Route planning Sociology Statistics Systems design Training Traveling salesman problem Tuning Vehicle routing vehicle routing problems Windows (intervals)
ISSN:	1089-778X, 1941-0026
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Abstract	Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to learn a model from vast data. In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of "training" problem instances, which is often referred to as PAP construction. However, compared to the traditional machine learning, PAP construction often suffers from the lack of training instances, and the obtained PAPs may fail to generalize well. This article proposes a novel competitive co-evolution scheme, named co-evolution of parameterized search (CEPS), as a remedy to this challenge. By co-evolving a configuration population and an instance population, CEPS is capable of obtaining generalizable PAPs with few training instances. The advantage of CEPS in improving generalization is analytically shown in this article. Two concrete algorithms, namely, CEPS-TSP and CEPS-VRPSPDTW, are presented for the traveling salesman problem (TSP) and the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW), respectively. The experimental results show that CEPS has led to better generalization, and even managed to find new best-known solutions for some instances.
AbstractList	Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to learn a model from vast data. In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of "training" problem instances, which is often referred to as PAP construction. However, compared to the traditional machine learning, PAP construction often suffers from the lack of training instances, and the obtained PAPs may fail to generalize well. This article proposes a novel competitive co-evolution scheme, named co-evolution of parameterized search (CEPS), as a remedy to this challenge. By co-evolving a configuration population and an instance population, CEPS is capable of obtaining generalizable PAPs with few training instances. The advantage of CEPS in improving generalization is analytically shown in this article. Two concrete algorithms, namely, CEPS-TSP and CEPS-VRPSPDTW, are presented for the traveling salesman problem (TSP) and the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW), respectively. The experimental results show that CEPS has led to better generalization, and even managed to find new best-known solutions for some instances.
Author	Tang, Ke Yao, Xin Liu, Shengcai Yang, Peng
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SubjectTerms	Algorithm configuration automatic parameter tuning co-evolution Evolutionary algorithms Machine learning Machine learning algorithms parallel algorithm portfolios (PAPs) Parallel algorithms Portfolios Route planning Sociology Statistics Systems design Training Traveling salesman problem Tuning Vehicle routing vehicle routing problems Windows (intervals)
Title	Few-Shots Parallel Algorithm Portfolio Construction via Co-Evolution
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