关于帕累托相容组合指标的构建

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Evolutionary Computation Pub Date : 2022-09-01 DOI:10.1162/evco_a_00307
J. G. Falcón-Cardona;M. T. M. Emmerich;C. A. Coello Coello
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引用次数: 2

摘要

质量指标(QI)最相关的性质是帕累托合规性,这意味着每当一个近似集在帕累托意义上严格支配另一个时,该指标必须反映这一点。超体积指标及其变体是已知的唯一符合帕累托的一元QIs,但也有许多常用的弱帕累托指标,如R2、IGD+和ε+。目前,一个开放的研究领域是寻找新的符合帕累托的指标,这些指标的偏好与超容量指标的偏好不同。在这篇文章中,我们提出了一个理论基础,将现有的弱帕累托相容性指标与至少一个帕累托一致性指标相结合,这样得到的组合指标也符合帕累托。最重要的是,我们证明了符合Pareto的QIs与弱符合Pareto指标的组合导致指标在最优点分布方面继承了弱符合指标的特性。这些新的组合指标的结果有三方面:(1)通过校正弱帕累托合规指标来增加可用的帕累托符合性合格中介机构的多样性;(2)引入合格中介机构组合的通用框架,以及(3)为多目标进化算法生成新的选择机制,其中可以实现/调整帕累托前沿上的期望分布。
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On the Construction of Pareto-Compliant Combined Indicators
The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect this. The hypervolume indicator and its variants are the only unary QIs known to be Pareto-compliant but there are many commonly used weakly Pareto-compliant indicators such as R2, IGD+, and ε+. Currently, an open research area is related to finding new Pareto-compliant indicators whose preferences are different from those of the hypervolume indicator. In this article, we propose a theoretical basis to combine existing weakly Pareto-compliant indicators with at least one being Pareto-compliant, such that the resulting combined indicator is Pareto-compliant as well. Most importantly, we show that the combination of Pareto-compliant QIs with weakly Pareto-compliant indicators leads to indicators that inherit properties of the weakly compliant indicators in terms of optimal point distributions. The consequences of these new combined indicators are threefold: (1) to increase the variety of available Pareto-compliant QIs by correcting weakly Pareto-compliant indicators, (2) to introduce a general framework for the combination of QIs, and (3) to generate new selection mechanisms for multiobjective evolutionary algorithms where it is possible to achieve/adjust desired distributions on the Pareto front.
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
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.
期刊最新文献
Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm. Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms. Genetic Programming for Automatically Evolving Multiple Features to Classification. A Tri-Objective Method for Bi-Objective Feature Selection in Classification. Preliminary Analysis of Simple Novelty Search.
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