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Genetic algorithm behavior in the MAXSAT domain [chapter]

Soraya Rana, Darrell Whitley
1998 Lecture Notes in Computer Science  
Yet MAXSAT problems exhibit extremely limited epistasis. Furthermore, all nonzero Walsh coe cients can be computed exactly for MAXSAT problems in polynomial time using only the clause information.  ...  This means the low order schema averages can be computed quickly and exactly for very large MAXSAT problems.  ...  Genetic algorithms are typically applied to black-box optimization problems; however, MAXSAT problems are not black-box optimization problems.  ... 
doi:10.1007/bfb0056920 fatcat:3kmr6hwnzvecbciveah5qzofqm

Optimal Neighborhood Preserving Visualization by Maximum Satisfiability

Kerstin Bunte, Matti Järvisalo, Jeremias Berg, Petri Myllymäki, Jaakko Peltonen, Samuel Kaski
2014 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
The method has a rigorous interpretation as optimal visualization based on the cost function.  ...  Unlike previous low-dimensional neighbor embedding methods, our formulation is guaranteed to yield globally optimal visualizations, and does so reasonably fast.  ...  ) an off-the-shelf MaxSAT solver is used to find an optimal MaxSAT solution.  ... 
doi:10.1609/aaai.v28i1.8974 fatcat:5uxtwj6kdffrpbur64yuaj32ny

Polarity and Variable Selection Heuristics for SAT-Based Anytime MaxSAT

Alexander Nadel
2020 Journal on Satisfiability, Boolean Modeling and Computation  
This paper is a system description of the anytime MaxSAT solver TT-Open-WBO-Inc, which won both of the weighted incomplete tracks of MaxSAT Evaluation 2019.  ...  We implemented the recently introduced polarity and variable selection heuristics, TORC and TSB, respectively, in the Open-WBO-Inc-BMO algorithm within the open-source anytime MaxSAT solver Open-WBO-Inc  ...  ) [3] and Bit-Vector Optimization (OBV) [15] , respectively.  ... 
doi:10.3233/sat-200126 fatcat:m5ozxjgct5anxdmdacayd2fooa

A MaxSAT-Based Framework for Group Testing

Lorenzo Ciampiconi, Bishwamittra Ghosh, Jonathan Scarlett, Kuldeep S Meel
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
The success of MaxSAT (maximum satisfiability) solving in recent years has motivated researchers to apply MaxSAT solvers in diverse discrete combinatorial optimization problems.  ...  Our extensive experimental results show that MGT can solve group testing instances of 10000 items with 3% defectivity, which no prior work can handle to the best of our knowledge.  ...  Borrowing terminology from the community focused on developing MaxSAT solvers, we are solving a partial weighted MaxSAT instance wherein we mark all the clauses with ∞ weight as hard, and clauses with  ... 
doi:10.1609/aaai.v34i06.6574 fatcat:dlayquqhgvbmxmqozlidcmo7um

Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem

Enrico S. Miranda, Fabio Fabris, Chrystian G. M. Nascimento, Alex A. Freitas, Alexandre C. M. Oliveira
2018 2018 7th Brazilian Conference on Intelligent Systems (BRACIS)  
As far as we know, this is the first time a meta-learning approach is proposed to select metaheuristics for solving a MaxSAT problem.  ...  Yet, choosing the best metaheuristic to solve a MaxSAT problem is hard, justifying the use of meta-learning algorithms for metaheuristic recommendation.  ...  There are several exact algorithms for solving MaxSAT problems, most are based on generic techniques for solving integer optimization problems [5] .  ... 
doi:10.1109/bracis.2018.00037 dblp:conf/bracis/MirandaFNFO18 fatcat:nxh36vg6ojbqhazbu3ftib7oui

A Denoising Autoencoder that Guides Stochastic Search [article]

Alexander W. Churchill and Siddharth Sigtia and Chrisantha Fernando
2014 arXiv   pre-print
A compressed hidden layer forces the autoencoder to learn hidden features in the training set that can be used to accelerate search on novel problems with similar structure.  ...  The algorithm outperforms a canonical genetic algorithm on several combinatorial optimisation problems, e.g. multidimensional 0/1 knapsack problem, MAXSAT, HIFF, and on parameter optimisation problems,  ...  The advantages over the GA are most clear on difficult combinatorial problems such as the 256-bit HIFF and MAXSAT, where the GA struggles to locate optimal solutions.  ... 
arXiv:1404.1614v1 fatcat:devdl3lxejafhpmw6iwt3z5y4u

MiFuMax—a Literate MaxSAT Solver

Mikoláš Janota
2015 Journal on Satisfiability, Boolean Modeling and Computation  
Even though the definition of MaxSAT may seem at first somewhat cryptic, it becomes far more intuitive once we observe that certain optimization problems can be encoded as MaxSAT.  ...  As the name suggests, the difference between unweighted MaxSAT and weighted MaxSAT is that soft clauses are labeled with weights. We will write (w, C) to denote a clause C with a weight w.  ... 
doi:10.3233/sat190103 fatcat:rhwzhrxewvecjofdoufafs2yba

Cost-optimal constrained correlation clustering via weighted partial Maximum Satisfiability

Jeremias Berg, Matti Järvisalo
2017 Artificial Intelligence  
For obtaining cost-optimal clusterings, we apply a state-of-the-art MaxSAT solver for solving the resulting MaxSAT instances optimally, resulting in cost-optimal clusterings.  ...  Integration of the fields of constraint solving and data mining and machine learning has recently been identified within the AI community as an important research direction with high potential.  ...  Solving MaxSAT Recent advances in MaxSAT solvers make MaxSAT a viable approach to finding globally (cost-)optimal solutions to various optimization problems with successful real-world applications such  ... 
doi:10.1016/j.artint.2015.07.001 fatcat:sx5xwpegtngu7oa7osd3eilwyu

Using Previous Models to Bias Structural Learning in the Hierarchical BOA

M. W. Hauschild, M. Pelikan, K. Sastry, D. E. Goldberg
2012 Evolutionary Computation  
We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.  ...  We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.  ...  It can be also be seen that the optimal value of p min decreases with problem size.  ... 
doi:10.1162/evco_a_00056 pmid:22082253 fatcat:bs4ojm5babgi5fkwayykmh3sbi

νZ - An Optimizing SMT Solver [chapter]

Nikolaj Bjørner, Anh-Dung Phan, Lars Fleckenstein
2015 Lecture Notes in Computer Science  
It allows users to pose and solve optimization problems modulo theories.  ...  of approaches for solving linear optimization problems over SMT formulas, MaxSMT, and their combinations.  ...  The type of the term t can be either Integer, Real or Bit-vector. -(minimize t ) -instruct the solver to minimize t .  ... 
doi:10.1007/978-3-662-46681-0_14 fatcat:wvqr3cehtvfujlr4evckb37fru

Using previous models to bias structural learning in the hierarchical BOA

Mark W. Hauschild, Martin Pelikan, Kumara Sastry, David E. Goldberg
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.  ...  We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.  ...  It can be also be seen that the optimal value of p min decreases with problem size.  ... 
doi:10.1145/1389095.1389172 dblp:conf/gecco/HauschildPSG08 fatcat:2qkby34zwzabhg3w6wew26zwkq

Boolean lexicographic optimization: algorithms & applications

Joao Marques-Silva, Josep Argelich, Ana Graça, Inês Lynce
2011 Annals of Mathematics and Artificial Intelligence  
This paper develops and evaluates algorithms for solving MOCO problems, defined on Boolean domains, and where the optimality criterion is lexicographic.  ...  that can exploit lexicographic optimization conditions in general MaxSAT problem instances.  ...  Acknowledgements Claude Michel provided insights on Lexicographic Optimization.  ... 
doi:10.1007/s10472-011-9233-2 fatcat:2mhgwr4skbestjaktr7rwxve4y

Iterative and core-guided MaxSAT solving: A survey and assessment

Antonio Morgado, Federico Heras, Mark Liffiton, Jordi Planes, Joao Marques-Silva
2013 Constraints  
In the last decade, different complete (or exact) algorithms have been proposed for solving MaxSAT to optimality.  ...  Solving MaxSAT is an important problem from both a theoretical and a practical point of view.  ...  The performance of all the algorithms is quite similar.Most of the algorithms solve between 60 and 37 instances, with BIT solving 60 instances and WMSU1 solving 37.  ... 
doi:10.1007/s10601-013-9146-2 fatcat:6hwa2oxk35exnn7lbdwcmux27a

MaxSAT by Improved Instance-Specific Algorithm Configuration

Carlos Ansotegui, Yuri Malitsky, Meinolf Sellmann
2014 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To this end, we employ the instance-specific algorithmconfigurator ISAC, and improve it with the latest inportfolio technology.  ...  In fact, the solvers presented here were independentlyevaluated at the 2013 MaxSAT Evaluation where they won six of the elevencategories.  ...  Introduction MaxSAT is the optimization version of the Satisfiability (SAT) problem.  ... 
doi:10.1609/aaai.v28i1.9128 fatcat:6jt73h56onfz3l7yytyedbyotu

MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules [chapter]

Dmitry Maliotov, Kuldeep S. Meel
2018 Lecture Notes in Computer Science  
The primary objective of the paper is to show that recent advances in the MaxSAT literature make it realistic to find optimal (or very high quality near-optimal) solutions to large-scale classification  ...  In experimental evaluations over a collection of benchmarks arising from practical scenarios, we demonstrate its effectiveness: we show that the formulation can solve large classification problems with  ...  Do advancements in MaxSAT solving enable MLIC to be run with datasets involving tens of thousands of variables with thousands of binary features? 2.  ... 
doi:10.1007/978-3-319-98334-9_21 fatcat:ybimexcrq5cghgqmgeidbwq554
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