Synopsis
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).
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About the Author
Xavier has studied computer science at Instituto Politécnico Nacional in México city and he has obtained master degree in computer science at CINVESTAV-IPN. He currently works at Oracle as a software developer.
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New Archive Based Evolutionary Multi-Objective Algorithms
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 de ne a new performance indicator, p, which can be viewed as an averaged Hausdor distance between the outcome set and the Pareto front and which is composed of (slight modi cations 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). 124 pp. Englisch. Seller Inventory # 9783659184963
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New Archive Based Evolutionary Multi-Objective Algorithms
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 de¿ne a new performance indicator, ¿p, which can be viewed as an 'averaged Hausdor¿ distance' between the outcome set and the Pareto front and which is composed of (slight modi¿cations 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).VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch. Seller Inventory # 9783659184963
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New Archive Based Evolutionary Multi-Objective Algorithms : Evolutionary Computation
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 de ne a new performance indicator, p, which can be viewed as an averaged Hausdor distance between the outcome set and the Pareto front and which is composed of (slight modi cations 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). Seller Inventory # 9783659184963
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New Archive Based Evolutionary Multi-Objective Algorithms | Evolutionary Computation
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Taschenbuch. Condition: Neu. New Archive Based Evolutionary Multi-Objective Algorithms | Evolutionary Computation | Xavier Esquivel | Taschenbuch | 124 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659184963 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106380634
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