Hierarchical Bayesian Optimization Algorithm : Toward a new Generation of Evolutionary Algorithms /
By: Pelikan, Martin [author.].
Contributor(s): SpringerLink (Online service).
Material type:
BookSeries: Studies in Fuzziness and Soft Computing: 170Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.Description: XVIII, 166 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540323730.Subject(s): Computer science | Computer programming | Computers | Artificial intelligence | Applied mathematics | Engineering mathematics | Algorithms | Computer Science | Theory of Computation | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Programming Techniques | Algorithms | Applications of MathematicsDDC classification: 004.0151 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK7644 |
From Genetic Variation to Probabilistic Modeling -- Probabilistic Model-Building Genetic Algorithms -- Bayesian Optimization Algorithm -- Scalability Analysis -- The Challenge of Hierarchical Difficulty -- Hierarchical Bayesian Optimization Algorithm -- Hierarchical BOA in the Real World.
This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The book focuses on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). BOA and hBOA are theoretically and empirically shown to provide robust and scalable solution for broad classes of nearly decomposable and hierarchical problems. A theoretical model is developed that estimates the scalability and adequate parameter settings for BOA and hBOA. The performance of BOA and hBOA is analyzed on a number of artificial problems of bounded difficulty designed to test BOA and hBOA on the boundary of their design envelope. The algorithms are also extensively tested on two interesting classes of real-world problems: MAXSAT and Ising spin glasses with periodic boundary conditions in two and three dimensions. Experimental results validate the theoretical model and confirm that BOA and hBOA provide robust and scalable solution for nearly decomposable and hierarchical problems with only little problem-specific information.
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