Bookcover of New Archive Based Evolutionary Multi-Objective Algorithms
Booktitle:

New Archive Based Evolutionary Multi-Objective Algorithms

Evolutionary Computation

LAP LAMBERT Academic Publishing (2012-07-14 )

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ISBN-13:

978-3-659-18496-3

ISBN-10:
3659184969
EAN:
9783659184963
Book language:
English
Blurb/Shorttext:
In this work we deal with the design of archive based multi-objective evolutionary algorithms (MOEAs) for the numerical treatment of multi objective optimization problems (MOPs). In particular, we design two generational operators­ one mutation and one crossover operator that are tailored to a class of archiving strategies and propose a new evolutionary strategy. Furthermore, we investigate here two widely used indicators for the evaluation of Multi-objective Evolutionary Algorithms, the Generational Distance (GD) and the Inverted Generational Distance (IGD), with respect to the properties of ametric. We define a new performance indicator, ∆p, which can be viewed as an ‘averaged Hausdorff distance’ between the outcome set and the Pareto front and which is composed of (slight modifications of) the well-known indicators Generational Distance (GD) and Inverted Generational Distance (IGD). We will discuss theoretical properties of ∆p (as well as for GD and IGD) such as the metric properties and the compliance with state-of-the-art multi-objective evolutionary algorithms (MOEAs).
Publishing house:
LAP LAMBERT Academic Publishing
Website:
https://www.lap-publishing.com/
By (author) :
Xavier Esquivel
Number of pages:
124
Published on:
2012-07-14
Stock:
Available
Category:
Informatics
Price:
59.00 €
Keywords:
Metric, multi-ob jective optimization, distance measurement

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