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Simultaneous Generalized Hill-Climbing Algorithms for Addressing Sets of Discrete Optimization Problems

Diane E. Vaughan, Sheldon H. Jacobson, Shane N. Hall, Laura A. McLay
2005 INFORMS journal on computing  
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]

Alexander G. Nikolaev, Sheldon H. Jacobson
2010 International Series in Operations Research and Management Science  
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]

Ehsan Shahamatnia, Ramin Ayanzadeh, Rita A. Ribeiro, Saeid Setayeshi
2011 IFIP Advances in Information and Communication Technology  
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]

Frederik Janssen, Johannes Fürnkranz
2009 Proceedings of the 2009 SIAM International Conference on Data Mining  
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

Mohammad Harun Rashid, Lixin Tao
2018 Advances in Science, Technology and Engineering Systems  
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]

Damion D., Charmane V., Emmanuel G., Jr., Oscar Chuy
2011 Recent Advances in Mobile Robotics  
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

Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S.V.N. Vishwanathan
2017 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17  
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

Kevin W. DeRonne, George Karypis
2006 Computational Systems Bioinformatics - Proceedings of the Conference CSB 2006  
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

Kevin W. DeRonne, George Karypis
2006 Computational Systems Bioinformatics - Proceedings of the Conference CSB 2006  
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

KEVIN W. DERONNE, GEORGE KARYPIS
2007 Journal of Bioinformatics and Computational Biology  
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

Kevin W. DeRonne, George Karypis
2006 Computational Systems Bioinformatics  
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

Teddy Candale, Sandip Sen
2006 ACM SIGecom Exchanges  
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

M. Harman, P. McMinn
2010 IEEE Transactions on Software Engineering  
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]

James Bevins, Rachel Slaybaugh
2018 arXiv   pre-print
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]

Briti Deb, Indrajit Mukherjee
2018 arXiv   pre-print
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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