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A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
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Cited by 2182 (27 self)
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. The core of this method is a simple hillclimbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distancebased method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment
When Will a Genetic Algorithm Outperform Hill Climbing?
 Advances in Neural Information Processing Systems 6
, 1993
"... We analyze a simple hillclimbing algorithm (RMHC) that was previously shown to outperform a genetic algorithm (GA) on a simple "Royal Road" function. We then analyze an "idealized" genetic algorithm (IGA) that is significantly faster than RMHC and that gives a lower bound for GA ..."
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Cited by 175 (2 self)
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We analyze a simple hillclimbing algorithm (RMHC) that was previously shown to outperform a genetic algorithm (GA) on a simple "Royal Road" function. We then analyze an "idealized" genetic algorithm (IGA) that is significantly faster than RMHC and that gives a lower bound
A late acceptance strategy in hillclimbing for examination timetabling problems
 In Proceedings of the conference on the Practice and Theory of Automated Timetabling(PATAT
, 2008
"... Over the years, many variants, extensions and adaptations of local search techniques have appeared in the literature. Some of them have become extremely famous, such as Simulated Annealing. Other ones have almost been forgotten, for example, “RecordtoRecord Travelling ” (Dueck 1993) or the “Old Ba ..."
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Cited by 15 (4 self)
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HillClimbing algorithm. Although its performance is known to be relatively worse than that of more sophisticated metaheuristics, it is still very popular thanks to its simplicity. It is also widely used in different hybridizations, such as guided, multistart or variable neighborhood search methods
Probabilistic HillClimbing
 Proceedings of Computational Learning Theory and 'Natural' Learning Systems
, 1991
"... Many learning tasks involve searching through a discrete space of performance elements, seeking an element whose future utility is expected to be high. As the task of finding the global optimum is often intractable, many practical learning systems use simple forms of hillclimbing to find a locally ..."
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Cited by 9 (3 self)
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Many learning tasks involve searching through a discrete space of performance elements, seeking an element whose future utility is expected to be high. As the task of finding the global optimum is often intractable, many practical learning systems use simple forms of hillclimbing to find a locally
Climbing Up NPHard Hills
 In Parallel Problem Solving from Nature IV
, 1996
"... . Evolutionary algorithms are sophisticated hillclimbers. In this paper, we discuss the ability of this class of local search algorithms to provide useful and efficient heuristics to solve NPhard problems. Our discussion is illustrated on experiments aiming at solving the jobshopscheduling pr ..."
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Cited by 2 (0 self)
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obtained by simple hillclimbing algorithms. We also show that our results compare favorably with other published results. 1 Introduction Our work aims at assessing what EAs are good for in the context of combinatorial optimization with regards to other search methods (both exact and non exact). We
When Will a Genetic Algorithm Outperform Hill Climbing?
 Advances in Neural Information Processing Systems 6
, 1993
"... We analyze a simple hillclimbing algorithm (RMHC) that was previously shown to outperform a genetic algorithm (GA) on a simple "Royal Road" function. We then analyze an "idealized" genetic algorithm (IGA) that is significantly faster than RMHC and that gives a lower bound fo ..."
Abstract
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We analyze a simple hillclimbing algorithm (RMHC) that was previously shown to outperform a genetic algorithm (GA) on a simple "Royal Road" function. We then analyze an "idealized" genetic algorithm (IGA) that is significantly faster than RMHC and that gives a lower bound
Better HillClimbing Searches for Parsimony
 IN: WABI
, 2003
"... The reconstruction of evolutionary trees is a major problem in biology, and many evolutionary trees are estimated using heuristics for the NPhard optimization problem Maximum Parsimony. The current heuristics for searching through tree space use a particular technique, called “treebisection and re ..."
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Cited by 16 (0 self)
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algorithm for computing the best 2ECR neighbors of a given tree, based upon a simple data structure which also allows us to efficiently calculate the best neighbors under NNI, SPR, and TBR operations (as well as efficiently running the greedy sequence addition technique for maximum parsimony). More
The Late Acceptance HillClimbing Heuristic
, 2012
"... This paper introduces a new and very simple search methodology called Late Acceptance HillClimbing (LAHC). It is a onepoint iterative search algorithm, which accepts nonimproving moves when a candidate cost function is better (or equal) than it was a number of iterations before. This value appear ..."
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Cited by 2 (0 self)
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This paper introduces a new and very simple search methodology called Late Acceptance HillClimbing (LAHC). It is a onepoint iterative search algorithm, which accepts nonimproving moves when a candidate cost function is better (or equal) than it was a number of iterations before. This value
HillClimbing Finds Random Planted Bisections
 Proc. 12th Symposium on Discrete Algorithms (SODA 01), ACM press, 2001
, 2001
"... We analyze the behavior of hillclimbing algorithms for the minimum bisection problem on instances drawn from the "planted bisection" random graph model, Gn;p;q , previously studied in [3, 4, 10, 12, 15, 9, 7]. This is one of the few problem distributions for which various popular heuristi ..."
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Cited by 18 (1 self)
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We analyze the behavior of hillclimbing algorithms for the minimum bisection problem on instances drawn from the "planted bisection" random graph model, Gn;p;q , previously studied in [3, 4, 10, 12, 15, 9, 7]. This is one of the few problem distributions for which various popular
The CN2 Induction Algorithm
 MACHINE LEARNING
, 1989
"... Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensib ..."
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Cited by 890 (6 self)
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Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple
Results 1  10
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