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On the Construction of Pareto-Compliant Combined Indicators
Author(s) -
Jesús Guillermo Falcón-Cardona,
Michael Emmerich,
Carlos A. Coello Coello
Publication year - 2022
Publication title -
evolutionary computation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.732
H-Index - 82
eISSN - 1530-9304
pISSN - 1063-6560
DOI - 10.1162/evco_a_00307
Subject(s) - pareto principle , pareto analysis , multi objective optimization , mathematical optimization , pareto interpolation , computer science , set (abstract data type) , pareto distribution , mathematics , generalized pareto distribution , statistics , extreme value theory , programming language
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 to those of the hypervolume indicator. In this paper, 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 Paretocompliant 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 multi-objective evolutionary algorithms where it is possible to achieve/adjust desired distributions on the Pareto front.

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