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Simultaneous Generalized Hill-Climbing Algorithms for Addressing Sets of Discrete Optimization Problems
2005
INFORMS journal on computing
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The resulting algorithms, termed simultaneous generalized hill climbing (SGHC) algorithms, can be applied to a wide variety of sets of related discrete optimization problems. ...
Generalized hill climbing (GHC) algorithms provide a framework for using local search algorithms to address intractable discrete optimization problems. ...
The Simultaneous Generalized Hill Climbing Algorithm Pseudo-Code SGHC algorithms provide a mathematical framework for addressing several fundamentally related discrete optimization problems simultaneously ...
doi:10.1287/ijoc.1040.0064
fatcat:ghijw4nyjjcovlqkmhuoyvo46q
Simulated Annealing
[chapter]
2010
International Series in Operations Research and Management Science
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It is typically used to address discrete and, to a lesser extent, continuous optimization problems. ...
Simulated annealing is a well-studied local search metaheuristic used to address discrete and, to a lesser extent, continuous optimization problems. ...
Acknowledgments This work is supported in part by the Air Force Office of Scientific Research (FA9550-07-1-0232). The authors wish to thank the anonymous referees for their feedback on this chapter. ...
doi:10.1007/978-1-4419-1665-5_1
fatcat:idntn7ghpnc6div67wrwchizru
Adaptive Imitation Scheme for Memetic Algorithms
[chapter]
2011
IFIP Advances in Information and Communication Technology
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Memetic algorithm, as a hybrid strategy, is an intelligent optimization method in problem solving. ...
In this paper a novel adaptive memetic algorithm has been developed in which the influence factor of environment on the learning abilities of each individual is set adaptively. ...
Proposed contributions have been developed for problems with continuous search space, using genetic algorithm and variation of hill climbing algorithm, but the concepts can be extended to discrete domain ...
doi:10.1007/978-3-642-19170-1_12
fatcat:yxt5pyb5ljcvdmvxzxzvze6gfi
A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning
[chapter]
2009
Proceedings of the 2009 SIAM International Conference on Data Mining
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Our results show that exhaustive search has primarily the effect of finding longer, but nevertheless more general rules than hill-climbing search. ...
Most commonly used inductive rule learning algorithms employ a hill-climbing search, whereas local pattern discovery algorithms employ exhaustive search. ...
We have observed over-searching primarily for heuristics where hill-climbing performed generally well, such as for the three heuristics that were optimized for hill-climbing search in [12, 13] . ...
doi:10.1137/1.9781611972795.29
dblp:conf/sdm/JanssenF09
fatcat:6lakl6eujbchnn36hyeirij3na
Parallelizing Combinatorial Optimization Heuristics with GPUs
2018
Advances in Science, Technology and Engineering Systems
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We have made a series of experiments with our proposed GPU framework to parallelize some heuristic methods such as simulated annealing, hill climbing, and genetic algorithm for solving combinatorial optimization ...
Heuristic methods which do not offer a convergence guarantee could obtain some satisfactory resolution for combinatorial optimization problems. ...
Very roughly, it deals with the problem of making optimal choices in huge discrete sets of alternatives. ...
doi:10.25046/aj030635
fatcat:566v2rey2bgyhfrjqqjvdnpihq
Motion Planning for Mobile Robots Via Sampling-Based Model Predictive Optimization
[chapter]
2011
Recent Advances in Mobile Robotics
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Hence, graph search algorithms can be applied to terminal constraint optimization problems and set point control problems. To observe this, consider the tree graph of Fig. 2 . ...
SBMPO can generate a trajectory for successful steep hill climbing, and it can also determine if the UGV needs to back up or how far the UGV needs to back up to successfully climb the hill. ...
doi:10.5772/17790
fatcat:pxu37qicsvgt7cl64iuevgwrbe
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
2017
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17
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2018 pre-print arXiv:1810.09558v1 fatcat:jg4o2swdu5gjlnncwsodlziu2i
We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. ...
We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. ...
ACKNOWLEDGMENTS The authors thank Charles Elkan, Sriram Srinavasan, Milos Curcic, Andrea Qualizza, Sham Kakade, Karthik Mohan, and Tao Hu for their helpful discussions. ...
doi:10.1145/3097983.3098184
dblp:conf/kdd/HillNLIV17
fatcat:qwdci7ij5zeoldt3lf7jhi4zoy
EFFECTIVE OPTIMIZATION ALGORITHMS FOR FRAGMENT-ASSEMBLY BASED PROTEIN STRUCTURE PREDICTION
2006
Computational Systems Bioinformatics - Proceedings of the Conference CSB 2006
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Two previously unused techniques are applied to the problem, called the Greedy algorithm and the Hill-climbing algorithm. ...
Experiments on a diverse set of 276 proteins show that the Hill-climbing algorithms consistently outperform existing approaches based on Simulated Annealing optimization (a traditional stochastic technique ...
Simulated Annealing (SA) Simulated Annealing 10 is a generalization of the Monte Carlo 14 method for discrete optimization problems. ...
doi:10.1142/18609475730013
fatcat:bziglujpfzbrvgujz5mpuqssca
EFFECTIVE OPTIMIZATION ALGORITHMS FOR FRAGMENT-ASSEMBLY BASED PROTEIN STRUCTURE PREDICTION
2006
Computational Systems Bioinformatics - Proceedings of the Conference CSB 2006
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Two previously unused techniques are applied to the problem, called the Greedy algorithm and the Hill-climbing algorithm. ...
Experiments on a diverse set of 276 proteins show that the Hill-climbing algorithms consistently outperform existing approaches based on Simulated Annealing optimization (a traditional stochastic technique ...
Simulated Annealing (SA) Simulated Annealing 10 is a generalization of the Monte Carlo 14 method for discrete optimization problems. ...
doi:10.1142/1860947573_0013
fatcat:str475xtkfe27nilfosa2llg3y
EFFECTIVE OPTIMIZATION ALGORITHMS FOR FRAGMENT-ASSEMBLY BASED PROTEIN STRUCTURE PREDICTION
2007
Journal of Bioinformatics and Computational Biology
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Two previously unused techniques are applied to the problem, called the Greedy algorithm and the Hill-climbing algorithm. ...
Experiments on a diverse set of 276 proteins show that the Hill-climbing algorithms consistently outperform existing approaches based on Simulated Annealing optimization (a traditional stochastic technique ...
Simulated Annealing (SA) Simulated Annealing 10 is a generalization of the Monte Carlo 14 method for discrete optimization problems. ...
doi:10.1142/s0219720007002618
pmid:17589965
fatcat:7wy2kqrx6bgmdmcnk3bb64ytv4
EFFECTIVE OPTIMIZATION ALGORITHMS FOR FRAGMENT-ASSEMBLY BASED PROTEIN STRUCTURE PREDICTION
2006
Computational Systems Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Two previously unused techniques are applied to the problem, called the Greedy algorithm and the Hill-climbing algorithm. ...
Experiments on a diverse set of 276 proteins show that the Hill-climbing algorithms consistently outperform existing approaches based on Simulated Annealing optimization (a traditional stochastic technique ...
Simulated Annealing (SA) Simulated Annealing 10 is a generalization of the Monte Carlo 14 method for discrete optimization problems. ...
doi:10.1142/9781860947575_0010
fatcat:ipe4mlrfvfejldxkrnbbzgejqa
A comparison of bidding strategies for simultaneous auctions
2006
ACM SIGecom Exchanges
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We investigate a hill-climbing bidding strategy, which is optimal given an infinite number of restarts, to decide on an agent's bid for simultaneous auctions. ...
We provide a comparison of this algorithm with existing ones, both in terms of utilities generated and computation time, along with a discussion of the strengths and weaknesses of these strategies. ...
The problem of computing optimal bids is more complex when bidding for multiple items. ...
doi:10.1145/1124566.1124572
fatcat:qbn6ilnrvnacrnxjezudnwbsfi
A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search
2010
IEEE Transactions on Software Engineering
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The results of this study reveal that cases exist of test data generation problem that suit each algorithm, thereby suggesting that a hybrid global-local search (a Memetic Algorithm) may be appropriate ...
However, despite the large number of recent studies on the applicability of different search based optimization approaches, there has been very little theoretical analysis of the types of testing problem ...
In order to address this problem, Hill Climbing is restarted at a new randomly chosen start point many times, until a budget of fitness evaluations has been exhausted.2.3 Hybrid Memetic Algorithm Approach ...
doi:10.1109/tse.2009.71
fatcat:vftsxsiqmzee3pftnfziaya4be
Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design
[article]
2018
arXiv
pre-print
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To demonstrate Gnowee's behavior for a variety of problem types, comparisons between Gnowee and several well-established metaheuristic algorithms are made for a set of eighteen continuous, mixed-integer ...
This novel algorithm was specifically developed to optimize complex nuclear design problems; the motivating research problem was the design of material stack-ups to modify neutron energy spectra to specific ...
Optimization problems can be classified by sub-setting the mathematical formulation in ways that are illustrative to assessing the requirements for a given optimization algorithm. ...
arXiv:1804.05429v1
fatcat:qk6qbjkquzfbhhprsxtkv32al4
Biclustering Using Modified Matrix Bandwidth Minimization and Biogeography-based Optimization
[article]
2018
arXiv
pre-print
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To optimize the bandwidth minimization problem, we adapted the Biogeography-based Optimization algorithm using logistic equation to model its migration rates. ...
Many practical problems require to find relationships between the two modes by simultaneously clustering the rows and columns, a problem commonly known as biclustering. ...
A hybrid of ant colony optimization with hill-climbing was proposed by [29] and particle swarm optimization with hill-climbing by [30] both for BMP. ...
arXiv:1807.07830v1
fatcat:ma5boah2kvap7b7btirjglbhfy
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