Results 231 to 240 of about 88,043 (267)
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Parallel evolutionary algorithms
Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694), 2004We are trying to piece together the knowledge of evolution with the help of biology, informatics and physics to create a complex evolutionary structure. It can speed up the creation of optimization algorithms with high quality features. The adaptive significance of GAs with sexual reproduction and an artificial immune system is presented. An artificial
Pavel Osmera +2 more
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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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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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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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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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IEEE Congress on Evolutionary Computation, 2010
FluidDB is a new structured storage system, available online for limited alpha test, which is designed to be able to easily store objects and relations among them (using tags). It is accessible through a simple REST interface, which is usually wrapped in a high-level language library.
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FluidDB is a new structured storage system, available online for limited alpha test, which is designed to be able to easily store objects and relations among them (using tags). It is accessible through a simple REST interface, which is usually wrapped in a high-level language library.
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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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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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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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Mastermind by evolutionary algorithms
Proceedings of the 1999 ACM symposium on Applied computing, 1999Luí Bento +2 more
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