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PALO: a probabilistic hill-climbing algorithm

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Russell GreinerAuthors Info & Claims

Artificial Intelligence, Volume 84, Issue 1-2

Pages 177 - 208

https://doi.org/10.1016/0004-3702(95)00040-2

Published: 01 July 1996 Publication History

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Liu YYou XLiu S(2022)Multi-ant colony optimization algorithm based on hybrid recommendation mechanismApplied Intelligence10.1007/s10489-021-02839-952:8(8386-8411)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10489-021-02839-9
Layeb A(2022)Tangent search algorithm for solving optimization problemsNeural Computing and Applications10.1007/s00521-022-06908-z34:11(8853-8884)Online publication date: 1-Jun-2022
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Pan HYou XLiu SZhang D(2021)Pearson correlation coefficient-based pheromone refactoring mechanism for multi-colony ant colony optimizationApplied Intelligence10.1007/s10489-020-01841-x51:2(752-774)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s10489-020-01841-x
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Published In

Artificial Intelligence Volume 84, Issue 1-2

July 1996

350 pages

ISSN:0004-3702

Editor:
Daniel G. Bobrow
Palo Alto Research Center, Palo Alto, CA

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Elsevier Science Publishers Ltd.

United Kingdom

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Published: 01 July 1996

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Liu YYou XLiu S(2022)Multi-ant colony optimization algorithm based on hybrid recommendation mechanismApplied Intelligence10.1007/s10489-021-02839-952:8(8386-8411)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10489-021-02839-9
Layeb A(2022)Tangent search algorithm for solving optimization problemsNeural Computing and Applications10.1007/s00521-022-06908-z34:11(8853-8884)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s00521-022-06908-z
Pan HYou XLiu SZhang D(2021)Pearson correlation coefficient-based pheromone refactoring mechanism for multi-colony ant colony optimizationApplied Intelligence10.1007/s10489-020-01841-x51:2(752-774)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s10489-020-01841-x
Heidari AAljarah IFaris HChen HLuo JMirjalili S(2019)An enhanced associative learning-based exploratory whale optimizer for global optimizationNeural Computing and Applications10.1007/s00521-019-04015-032:9(5185-5211)Online publication date: 29-Jan-2019
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Ceren RDoshi PBanerjee BJonker CMarsella SThangarajah JTuyls K(2016)Reinforcement Learning in Partially Observable Multiagent SettingsProceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems10.5555/2936924.2937002(530-538)Online publication date: 9-May-2016
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Cobo LIsbell CThomaz AGini MShehory OIto TJonker C(2013)Object focused q-learning for autonomous agentsProceedings of the 2013 international conference on Autonomous agents and multi-agent systems10.5555/2484920.2485087(1061-1068)Online publication date: 6-May-2013
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Wu ZChow T(2013)Neighborhood field for cooperative optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-012-0955-917:5(819-834)Online publication date: 1-May-2013
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Jaroszewicz SScheffer TGrossman RBayardo RBennett K(2005)Fast discovery of unexpected patterns in data, relative to a Bayesian networkProceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining10.1145/1081870.1081887(118-127)Online publication date: 21-Aug-2005
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Watanabe O(2005)Sequential sampling techniques for algorithmic learning theoryTheoretical Computer Science10.1016/j.tcs.2005.09.003348:1(3-14)Online publication date: 2-Dec-2005
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Hanlon PLorenz WStrenski D(2003)Least-squares fit of genomic data by sums of epistatic effectsJournal of Parallel and Distributed Computing10.1016/S0743-7315(03)00083-263:7-8(683-691)Online publication date: 1-Jul-2003
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Late-acceptance hill-climbing with a grammatical program representation

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