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Coevolutionary Free Lunches
"... Abstract — Recent work on the foundational underpinnings of blackbox optimization has begun to uncover a rich mathematical structure. In particular, it is now known that an inner product between the optimization algorithm and the distribution of optimization problems likely to be encountered fixes ..."
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the distribution over likely performances in running that algorithm. One ramification of this is the “No Free Lunch ” (NFL) theorems, which state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem
Coevolutionary Free Lunches
"... Abstract — ecent work on the foundations of optimization has begun to uncover its underlying rich structure. In particular, the “No Free Lunch ” (NFL) theorems [WM97] state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the nee ..."
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Abstract — ecent work on the foundations of optimization has begun to uncover its underlying rich structure. In particular, the “No Free Lunch ” (NFL) theorems [WM97] state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights
No Free Lunch Theorems for Search
, 1995
"... We show that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions. In particular, if algorithm A outperforms algorithm B on some cost functions, then loosely speaking there must exist exactly as many other functions wh ..."
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We show that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions. In particular, if algorithm A outperforms algorithm B on some cost functions, then loosely speaking there must exist exactly as many other functions
Complexity Theory and the No Free Lunch Theorem
, 2005
"... Introduction This tutorial reviews basic concepts in complexity theory, as well as various No Free Lunch results and how these results relate to computational complexity. The tutorial explain basic concepts in an informal fashion that illuminates key concepts. "No Free Lunch" theorems for ..."
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Cited by 18 (0 self)
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Introduction This tutorial reviews basic concepts in complexity theory, as well as various No Free Lunch results and how these results relate to computational complexity. The tutorial explain basic concepts in an informal fashion that illuminates key concepts. "No Free Lunch" theorems
A maximum likelihood approach to continuous speech recognition
 IEEE Trans. Pattern Anal. Machine Intell
, 1983
"... AbstractSpeech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the ..."
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Cited by 472 (9 self)
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AbstractSpeech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining
A New Method for Solving Hard Satisfiability Problems
 AAAI
, 1992
"... We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approac ..."
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Cited by 734 (21 self)
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discussed. GSAT is best viewed as a modelfinding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theoremproving problems as modelfinding tasks.
Free Lunches for Function and Program Induction
"... In this paper we prove that for a variety of practical problems and representations, there is a free lunch for search algorithms that specialise in the task of finding functions or programs that solve problems, such as genetic programming. In other words, not all such algorithms are equally good und ..."
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Cited by 4 (0 self)
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In this paper we prove that for a variety of practical problems and representations, there is a free lunch for search algorithms that specialise in the task of finding functions or programs that solve problems, such as genetic programming. In other words, not all such algorithms are equally good
Cyberguide: A Mobile ContextAware Tour Guide
, 1996
"... Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, suach as position, to offer greater services to the user. In his paper, we present the Cyberguide project, in which we are buil ..."
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Cited by 642 (24 self)
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Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, suach as position, to offer greater services to the user. In his paper, we present the Cyberguide project, in which we
The No Free Lunch Theorems: Complexity and Security
, 2003
"... One of the main challenges for decision scientists in the 21st century will be managing systems of ever increasing complexity. As systems like electrical power grids, computer networks, and the software that controls it all grow increasingly complex, fragility, bugs, and security flaws are becoming ..."
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Cited by 5 (1 self)
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increasingly prevalent and problematic. It is natural then to ask what consequences this growing complexity has on our ability to manage these systems. In this paper, we take a first step toward addressing this question with the development of the Fundamental matrix, a framework for analyzing the broad
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 800 (26 self)
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The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical
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