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A Destructive Evolutionary Algorithm Process
2007 Frontiers in the Convergence of Bioscience and Information Technologies, 2007This paper describes the application of evolutionary search to the problem of Flash memory wear-out. The current method for establishing memory operating parameters is a time consuming and expensive manual process of destructive testing. Understandably this process is normally undertaken only at design time.
Joe Sullivan, Conor Ryan
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Herding Evolutionary Algorithm
Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015In this paper, we address the problem of black box optimization over binary vectors. We introduce a novel evolutionary algorithm called Herding Evolutionary Algorithm which relies on herding to generate individuals with empirical moments close to those of selected individuals. We report experiments with diverse fitness functions and compare the results
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Bull. EATCS, 1999
Summary: We discuss the paradigm of Evolutionary Algorithms (EAs). We argue about the need for new heuristics in real-world solving, compare the evolutionary approach to local search, simulated annealing, and tabu search, and indicate the potential behind the evolutionary approach.
David B. Fogel, Zbigniew Michalewicz
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Summary: We discuss the paradigm of Evolutionary Algorithms (EAs). We argue about the need for new heuristics in real-world solving, compare the evolutionary approach to local search, simulated annealing, and tabu search, and indicate the potential behind the evolutionary approach.
David B. Fogel, Zbigniew Michalewicz
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What is an Evolutionary Algorithm?
2003The most important aim of this chapter is to describe what an evolutionary algorithm is. This description is deliberately based on a unifying view presenting a general scheme that forms the common basis of all evolutionary algorithm (EA) variants. The main components of EAs are discussed, explaining their role and related issues of terminology. This is
A. E. Eiben, J. E. Smith
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Multinational evolutionary algorithms
Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 2003Since practical problems often are very complex with a large number of objectives, it can be difficult or impossible to create an objective function expressing all the criteria of good solutions. Sometimes a simpler function can be used where local optimas could be both valid and interesting.
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Geometry of evolutionary algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011The various flavors of Evolutionary Algorithms look very similar when cleared of algorithmically irrelevant differences such as domain of application and phenotype interpretation. Representation-independent algorithmic characteristics like the selection scheme can be freely exchanged between algorithms.
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An Evolutionary Clustering Algorithm
2004There are many heuristic algorithms for clustering, from which the most important are the hierarchical methods of agglomeration, especially the Ward’s method. Among the iterative methods the most universally used is the C–means method and it’s generalizations.
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Evolutionary Algorithms and Their Applications to Engineering Problems
Neural Computing and Applications, 2020Halina Kwaśnicka, Adam Slowik
exaly
Many-Objective Evolutionary Algorithms
ACM Computing Surveys, 2015Xin Yao, Ke Tang, Jinlong Li
exaly