Dynamic Compartmental Models for Large Multi-objective Landscapes and Performance Estimation

Dynamic Compartmental Models are linear models inspired by epidemiology models to study Multi- and Many-Objective Evolutionary Algorithms dynamics. So far they have been tested on small MNK-Landscapes problems with 20 variables and used as a tool for algorithm analysis, algorithm comparison, and alg...

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Published inEvolutionary Computation in Combinatorial Optimization Vol. 12102; pp. 99 - 113
Main Authors Monzón, Hugo, Aguirre, Hernán, Verel, Sébastien, Liefooghe, Arnaud, Derbel, Bilel, Tanaka, Kiyoshi
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Switzerland Springer International Publishing AG 01.01.2020
Springer International Publishing
Springer
SeriesLecture Notes in Computer Science
Subjects
Artificial Intelligence
Compartmental models
Computer Science
Hypervolume estimation
Mathematics
Modeling
Multi-objective optimization
Operations Research
Optimization and Control
Population dynamics
Compartmental models
Multi-objective optimization
Population dynamics
Hypervolume estimation
Modeling
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ISBN9783030436797
3030436799
3030436802
9783030436803
ISSN0302-9743
1611-3349
1611-3349
DOI10.1007/978-3-030-43680-3_7

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Summary:Dynamic Compartmental Models are linear models inspired by epidemiology models to study Multi- and Many-Objective Evolutionary Algorithms dynamics. So far they have been tested on small MNK-Landscapes problems with 20 variables and used as a tool for algorithm analysis, algorithm comparison, and algorithm configuration assuming that the Pareto optimal set is known. In this paper, we introduce a new set of features based only on when non-dominated solutions are found in the population, relaxing the assumption that the Pareto optimal set is known in order to use Dynamic Compartment Models on larger problems. We also propose an auxiliary model to estimate the hypervolume from the features of population dynamics that measures the changes of new non-dominated solutions in the population. The new features are tested by studying the population changes on the Adaptive $$\epsilon $$ -Sampling $$\epsilon $$ -Hood while solving 30 instances of a 3 objective, 100 variables MNK-landscape problem. We also discuss the behavior of the auxiliary model and the quality of its hypervolume estimations.
Bibliography:Original Abstract: Dynamic Compartmental Models are linear models inspired by epidemiology models to study Multi- and Many-Objective Evolutionary Algorithms dynamics. So far they have been tested on small MNK-Landscapes problems with 20 variables and used as a tool for algorithm analysis, algorithm comparison, and algorithm configuration assuming that the Pareto optimal set is known. In this paper, we introduce a new set of features based only on when non-dominated solutions are found in the population, relaxing the assumption that the Pareto optimal set is known in order to use Dynamic Compartment Models on larger problems. We also propose an auxiliary model to estimate the hypervolume from the features of population dynamics that measures the changes of new non-dominated solutions in the population. The new features are tested by studying the population changes on the Adaptive \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Sampling \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}-Hood while solving 30 instances of a 3 objective, 100 variables MNK-landscape problem. We also discuss the behavior of the auxiliary model and the quality of its hypervolume estimations.
ISBN:9783030436797
3030436799
3030436802
9783030436803
ISSN:0302-9743
1611-3349
1611-3349
DOI:10.1007/978-3-030-43680-3_7