Titlebook: Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms; Oliver Schütze,Carlos Hernández Book 2021 The Editor(s) (if

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John Garthwaite,Eric Southam,Stephen J. EastIn this chapter, we discuss the framework we are using for the analysis of the archiving strategies we will discuss in the sequel.

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Christian Vannier,Antoine TrillerIn the following three chapters we will address archivers that aim for Pareto front approximations of a given MOP. In the first step, we are interested in maintaining . non-dominated solutions that were computed during the run of an algorithm. That is, if we are given a set of candidate solutions .

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Introduction,In a variety of applications, one is faced with the problem to optimize several conflicting and incommensurable objectives concurrently.

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Multi-objective Optimization,In this section, we give a brief introduction to continuous multi-objective optimization including the most important notations and definitions. After this, we will give a short overview on existing methods for the numerical treatment of such problems.

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Archiving in Evolutionary Multi-objective Optimization: A Short Overview,As discussed in the previous chapter, one can expect that the solution set of a continuous MOP with . objectives forms—at least locally—an object of dimension ..

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The Framework,In this chapter, we discuss the framework we are using for the analysis of the archiving strategies we will discuss in the sequel.

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Computing the Entire Pareto Front,In the following three chapters we will address archivers that aim for Pareto front approximations of a given MOP. In the first step, we are interested in maintaining . non-dominated solutions that were computed during the run of an algorithm. That is, if we are given a set of candidate solutions .

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https://doi.org/10.1007/978-3-030-63773-6Computational Intelligence; Archiving Strategies; Evolutionary Multi-objective Optimization Algorithms

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