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Hierarchical Bayesian Optimization Algorithm
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This chapter describes hBOA and its predecessor, the Bayesian optimization algorithm (BOA), and outlines some of the most important theoretical and empirical results in this line of research.About:
The article was published on 2005-01-01 and is currently open access. It has received 109 citations till now. The article focuses on the topics: Variable-order Bayesian network & Bayesian hierarchical modeling.read more
Citations
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An introduction and survey of estimation of distribution algorithms
Mark W. Hauschild,Martin Pelikan +1 more
TL;DR: Estimation of distribution algorithms are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions and many of the different types of EDAs are outlined.
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Insights on Transfer Optimization: Because Experience is the Best Teacher
TL;DR: A general formalization of transfer optimization is introduced, based on which the conceptual realizations of the paradigm are classified into three distinct categories, namely sequential transfer , multitasking, and multiform optimization.
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2009 Special Issue: Intelligence in the brain: A theory of how it works and how to build it
TL;DR: A theory of how general-purpose learning-based intelligence is achieved in the mammal brain, and how to replicate it is presented, and empirical results which fit the theory are reviewed, and important new directions for research are suggested.
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Generalized decomposition and cross entropy methods for many-objective optimization
TL;DR: The proposed generalized decomposition algorithm - MACE-gD - is shown to be highly competitive with the existing best-in-class decomposition-based algorithm (MOEA/D) and a more elaborate EDA method (RM-MEDA).
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Prediction of road accidents: A Bayesian hierarchical approach
TL;DR: It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any road network provided that the required data are available.
References
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Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book
The Sciences of the Artificial
TL;DR: A new edition of Simon's classic work on artificial intelligence as mentioned in this paper adds a chapter that sorts out the current themes and tools for analyzing complexity and complex systems, taking into account important advances in cognitive psychology and the science of design while confirming and extending Simon's basic thesis that a physical symbol system has the necessary and sufficient means for intelligent action.
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Paper: Modeling by shortest data description
TL;DR: The number of digits it takes to write down an observed sequence x1,...,xN of a time series depends on the model with its parameters that one assumes to have generated the observed data.