A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jaume Jordan, Javier Palanca, Victor Sanchez-Anguix, Vicente Julian
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引用次数: 0

Abstract

This article presents a new crossover operator for problems with an underlying graph structure where edges point to prospective interdependence relationships between decision variables and neighborhoods shape the definition of the global objective function via a sum of different expressions, one for each neighborhood. The main goal of this work is to propose a crossover operator that is broadly applicable, adaptable, and effective across a wide range of problem settings characterized by objective functions that are expressed in terms of graph neighbourhoods with interdependent and related variables. Extensive experimentation has been conducted to compare and evaluate the proposed crossover operator with both classic and specialized crossover operators. More specifically, the crossover operators have been tested under a variety of graph types, which model how variables are involved in interdependencies, different types of expressions in which interdependent variables are combined, and different numbers of decision variables. The results suggest that the new crossover operator is statistically better or at least as good as the best-performing crossover in 75% of the families of problems tested.

具有相互依赖和相关变量的图邻域上目标函数的交叉算子
本文提出了一种新的交叉算子,用于具有底层图结构的问题,其中边指向决策变量和邻域之间的预期相互依赖关系,通过不同表达式的总和来塑造全局目标函数的定义,每个邻域一个。这项工作的主要目标是提出一种跨界算子,该算子在广泛的问题设置中广泛适用,适应性强,并且有效,这些问题设置的特征是目标函数,这些目标函数以具有相互依赖和相关变量的图邻域表示。我们进行了大量的实验来比较和评估所提出的交叉算子与经典的和专门的交叉算子。更具体地说,交叉算子已经在各种图类型下进行了测试,这些图类型描述了变量是如何相互依赖的,相互依赖的变量组合的不同类型的表达式,以及不同数量的决策变量。结果表明,在75%的问题测试家庭中,新的交叉操作在统计上更好,或者至少与表现最好的交叉操作一样好。
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
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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