Hierarchical Bayesian Optimization Algorithm
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Hierarchical Bayesian Optimization Algorithm
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Author : Martin Pelikan
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-02
Hierarchical Bayesian Optimization Algorithm written by Martin Pelikan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02 with Computers categories.
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.
Bayesian Optimization Algorithm
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Author : Martin Pelikan
language : en
Publisher:
Release Date : 2002
Bayesian Optimization Algorithm written by Martin Pelikan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.
Using Hierarchical Bayesian Optimization To Learn And Exploit The Dependency Structures Of Combinatorial Many Objective Decision Problems
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Author : Ruchit Aswin Shah
language : en
Publisher:
Release Date : 2010
Using Hierarchical Bayesian Optimization To Learn And Exploit The Dependency Structures Of Combinatorial Many Objective Decision Problems written by Ruchit Aswin Shah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Bayesian Methods For Knowledge Transfer And Policy Search In Reinforcement Learning
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Author : Aaron Creighton Wilson
language : en
Publisher:
Release Date : 2012
Bayesian Methods For Knowledge Transfer And Policy Search In Reinforcement Learning written by Aaron Creighton Wilson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Bayesian statistical decision theory categories.
How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented knowledge when selecting actions. Our first contribution introduces the multi-task Reinforcement Learning setting in which an agent solves a sequence of tasks. An agent equipped with knowledge of the relationship between tasks can transfer knowledge between them. We propose the transfer of two distinct types of knowledge: knowledge of domain models and knowledge of policies. To represent the transferable knowledge, we propose hierarchical Bayesian priors on domain models and policies respectively. To transfer domain model knowledge, we introduce a new algorithm for model-based Bayesian Reinforcement Learning in the multi-task setting which exploits the learned hierarchical Bayesian model to improve exploration in related tasks. To transfer policy knowledge, we introduce a new policy search algorithm that accepts a policy prior as input and uses the prior to bias policy search. A specific implementation of this algorithm is developed that accepts a hierarchical policy prior. The algorithm learns the hierarchical structure and reuses components of the structure in related tasks. Our second contribution addresses the basic problem of generalizing knowledge gained from previously-executed policies. Bayesian Optimization is a method of exploiting a prior model of an objective function to quickly identify the point maximizing the modeled objective. Successful use of Bayesian Optimization in Reinforcement Learning requires a model relating policies and their performance. Given such a model, Bayesian Optimization can be applied to search for an optimal policy. Early work using Bayesian Optimization in the Reinforcement Learning setting ignored the sequential nature of the underlying decision problem. The work presented in this thesis explicitly addresses this problem. We construct new Bayesian models that take advantage of sequence information to better generalize knowledge across policies. We empirically evaluate the value of this approach in a variety of Reinforcement Learning benchmark problems. Experiments show that our method significantly reduces the amount of exploration required to identify the optimal policy. Our final contribution is a new framework for learning parametric policies from queries presented to an expert. In many domains it is difficult to provide expert demonstrations of desired policies. However, it may still be a simple matter for an expert to identify good and bad performance. To take advantage of this limited expert knowledge, our agent presents experts with pairs of demonstrations and asks which of the demonstrations best represents a latent target behavior. The goal is to use a small number of queries to elicit the latent behavior from the expert. We formulate a Bayesian model of the querying process, an inference procedure that estimates the posterior distribution over the latent policy space, and an active procedure for selecting new queries for presentation to the expert. We show, in multiple domains, that the algorithm successfully learns the target policy and that the active learning strategy generally improves the speed of learning.
Scalable Optimization Via Probabilistic Modeling
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Author : Martin Pelikan
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-25
Scalable Optimization Via Probabilistic Modeling written by Martin Pelikan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-25 with Mathematics categories.
I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.
Exploitation Of Linkage Learning In Evolutionary Algorithms
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Author : Ying-ping Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-04-16
Exploitation Of Linkage Learning In Evolutionary Algorithms written by Ying-ping Chen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-16 with Technology & Engineering categories.
One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues.
Scalable Optimization Via Probabilistic Modeling
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Author : Martin Pelikan
language : en
Publisher: Springer
Release Date : 2007-01-12
Scalable Optimization Via Probabilistic Modeling written by Martin Pelikan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-12 with Mathematics categories.
I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.
Computational Intelligence And Intelligent Systems
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Author : Kangshun Li
language : en
Publisher: Springer
Release Date : 2016-01-18
Computational Intelligence And Intelligent Systems written by Kangshun Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-18 with Computers categories.
This book constitutes the refereed proceedings of the 7th International Symposium on Intelligence Computation and Applications, ISICA 2015, held in Guangzhou, China, in November 2015. The 77 revised full papers presented were carefully reviewed and selected from 189 submissions. The papers feature the most up-to-date research in analysis and theory of evolutionary computation, neural network architectures and learning; neuro-dynamics and neuro-engineering; fuzzy logic and control; collective intelligence and hybrid systems; deep learning; knowledge discovery; learning and reasoning.
Evolutionary Optimization Algorithms
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Author : Dan Simon
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-13
Evolutionary Optimization Algorithms written by Dan Simon and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-13 with Mathematics categories.
A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.
Building Block Identification By Simultaneity Matrix
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Author :
language : en
Publisher:
Release Date : 2004
Building Block Identification By Simultaneity Matrix written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Genetic algorithms categories.
The simultaneity matrix is an l x l matrix of numbers. The matrix is constructed according to a set of l-bit solutions. The matrix element m[subscript ij] is the degree of linkage between bit positions i and j. We partition {0,...,l-1} by putting i and j in the same partition subset if m[subscript ij] is significantly high. The partition represents the bit positions of building blocks. The partition is exploited in solution recombination so that the bits governed by the same partition subset are passed together. It can be shown that identifying building blocks by the simultaneity matrix can solve the additively decomposable functions (ADFs) and hierarchically decomposable functions (HDFs) in a polynomial relationship between the number of function evaluations required to reach the optimum and the problem size. A comparison to the hierarchical Bayesian optimization algorithm (hBOA) is made. The hBOA uses less number of function evaluations than that ofour algorithm. However, computing the matrix is 10 times faster and uses 10 times less memory than constructing Bayesian network.