Feature-Based Diversity Optimization for Problem Instance Classification

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

Evolutionary Computation Pub Date : 2021-03-02 DOI:10.1162/evco_a_00274

Wanru Gao;Samadhi Nallaperuma;Frank Neumann

引用次数: 47

Abstract

Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Travelling Salesperson Problem (TSP). In this article, we present a general framework that is able to construct a diverse set of instances which are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances which are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.

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基于特征的问题实例分类多样性优化

理解启发式搜索方法的行为是一个挑战。这甚至适用于简单的本地搜索方法，如旅行推销员问题（TSP）的2-OPT。在本文中，我们提出了一个通用框架，该框架能够构造一组不同的实例，这些实例对于给定的搜索启发式来说是困难的或容易的。这样的多样性集合是通过使用进化算法来构建针对潜在问题的不同特征而多样的硬实例或易实例来获得的。通过检查构建的实例集，我们发现两个或三个特征的许多组合可以很好地分类TSP实例，即它们是否难以通过2-OPT解决。

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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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.