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Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach

Khalid Haddouch, Karim Elmoutaoukil, Mohamed Ettaouil
2016 International Journal of Interactive Multimedia and Artificial Intelligence  
A wide variety of real world optimization problems can be modelled as Weighted Constraint Satisfaction Problems (WCSPs).  ...  In this regard, our approach recognizes the optimal solution of the said instances.  ...  Conclusions In this paper, we have proposed a new approach for solving binary weighted constraint satisfaction problems.  ... 
doi:10.9781/ijimai.2016.4111 fatcat:dxkpejh5wncq3npk7bddfdpf3u

Page 431 of The Journal of the Operational Research Society Vol. 59, Issue 4 [page]

2008 The Journal of the Operational Research Society  
fuzzy-neural approach for constraint satisfaction of a generalized job shop scheduling problem (GJSSP) fuzzy processing times.  ...  Furthermore, Yang and Wang (2000) proposed a constraint satisfaction adaptive neural network (CSANN) for the  ... 

Applying Hopfield Neural Networks To Solve CSP Problems

Anatolii Balanda
2020 International Journal of Advanced Trends in Computer Science and Engineering  
The article reviews methods based on the Hopfield neural network for solving CSP and FCSP problems.  ...  In the field of artificial intelligence, there is a class of combinatorial problems called CSP problems (Constraint satisfaction Problems).  ...  We consider it in a narrower context, namely in thecontext of solving constraint optimization problems, or, in other words, for constraint satisfaction problems with flexible constraints, where the goal  ... 
doi:10.30534/ijatcse/2020/77942020 fatcat:3tiavgn5djfdvjwdryqrdbwiv4

Neural Network and Local Search to Solve Binary CSP

Adil Bouhouch, Hamid Bennis, Chakir Loqman, Abderrahim El Qadi
2018 Indonesian Journal of Electrical Engineering and Computer Science  
<p>Continuous Hopfield neural Network (CHN) is one of the effective approaches to solve Constrain Satisfaction Problems (CSPs).  ...  In this paper, we propose a new hybrid approach combining CHN and min-conflict heuristic to mitigate these problems.  ...  As for exact approaches, most of them have the backtracking algorithm (BT) as a main algorithm for solving constraint satisfaction problems.  ... 
doi:10.11591/ijeecs.v10.i3.pp1319-1330 fatcat:4pxngylyg5bxddxgc67kr7r4aq


Sridhar K, Prakash T. Lazarus
In this paper an attempt is made to present a Constraint Satisfaction Adaptive Neural Network (CSANN) to solve the generalized job-shop scheduling problem and it shows how to map a difficult constraint  ...  satisfaction job-shop scheduling problem onto a simple neural net, where the number of neural processors equals the number of operations, and the number of interconnections grows linearly with the total  ...  CONSTRAINT SATISFACTION ADAPTIVE NEURAL NETWORK For the methodology to solve the scheduling problem, applicability of a constraint satisfaction adaptive neural network is considered.  ... 
doi:10.34218/ijierd.5.3.2014.003 fatcat:oci652lefrbdxcpki2st6cbg4q

A neuro-evolutionary approach to produce general hyper-heuristics for the dynamic variable ordering in hard binary constraint satisfaction problems

José Carlos Ortiz-Bayliss, Hugo Terashima-Marin, Peter Ross, Jorge Iván Fuentes-Rosado, Manuel Valenzuela-Rendón
2009 Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09  
This paper introduces a neuro-evolutionary approach to produce hyper-heuristics for the dynamic variable ordering for hard binary constraint satisfaction problems.  ...  The model uses a GA to evolve a population of neural networks architectures and parameters. For every cycle in the GA process, the new networks are trained using backpropagation.  ...  INTRODUCTION A Constraint Satisfaction Problem (CSP) is defined by a set of variables and a set of constraints. Each variable has a nonempty domain of possible values.  ... 
doi:10.1145/1569901.1570174 dblp:conf/gecco/Ortiz-BaylissTRFV09 fatcat:3k7qerpysje2da5bkocl5xpss4

Page 7844 of Mathematical Reviews Vol. , Issue 2003j [page]

2003 Mathematical Reviews  
The paper analyzes simple standard approaches to solving binary constraint satisfaction problems (BCSPs): backtrack-free search, strong k-consistency, and search with backtracking based on tests for inconsistency  ...  Summary: “A novel artificial neural network heuristic (ANN) for general constraint satisfaction problems is presented, extending a recently suggested method restricted to Boolean variables.  ... 

Neural Networks to Guide the Selection of Heuristics within Constraint Satisfaction Problems [chapter]

José Carlos Ortiz-Bayliss, Hugo Terashima-Marín, Santiago Enrique Conant-Pablos
2011 Lecture Notes in Computer Science  
We propose a neural network hyper-heuristic approach for variable ordering within Constraint Satisfaction Problems.  ...  When solving a Constraint Satisfaction Problem, the order in which the variables are selected to be instantiated has implications in the complexity of the search.  ...  Acknowledgments This research was supported in part by ITESM under the Research Chair CAT-144 and the CONACYT Project under grant 99695.  ... 
doi:10.1007/978-3-642-21587-2_27 fatcat:nzorknconfco7fk7hcgvdhfscm

Polytopic Input Constraints in Learning-Based Optimal Control Using Neural Networks [article]

Lukas Markolf, Olaf Stursberg
2021 arXiv   pre-print
The second approach makes use of neural networks with softmax output units to map states into parameters, which determine (sub-)optimal inputs by a convex combination of the vertices of the input constraint  ...  The approach allows to consider state-dependent input constraints, as well as to ensure the satisfaction of state constraints by exploiting recursive reachable set computations.  ...  The authors are with the Control and System Theory Group, Dept. of Electrical Engineering and Computer Science, University of Kassel, Germany. {lukas.markolf, stursberg}  ... 
arXiv:2105.03376v1 fatcat:2ehwceasgfh4tg2vw3brhhiqaq

Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling

S. Yang, Dingwei Wang
2000 IEEE Transactions on Neural Networks  
This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction  ...  Index Terms-Adaptive neural network, constraint satisfaction, generalized job-shop scheduling problem, heuristic.  ...  ACKNOWLEDGMENT The authors would like to thank the reviewers for their helpful comments and suggestions that contributed to improve the quality of this paper.  ... 
doi:10.1109/72.839016 pmid:18249776 fatcat:3z3ptgj3t5d2tiswbgu36i7uoq

Training Recurrent Neural Networks as a Constraint Satisfaction Problem [article]

Hamid Khodabandehlou, M. Sami Fadali
2018 arXiv   pre-print
This paper presents a new approach for training artificial neural networks using techniques for solving the constraint satisfaction problem (CSP).  ...  The quotient gradient system (QGS) is a trajectory-based method for solving the CSP. This study converts the training set of a neural network into a CSP and uses the QGS to find its solutions.  ...  NOVEL is another hybrid approach which uses a trajectory-based method to find feasible Training Recurrent Neural Networks as a Constraint Satisfaction Problem Hamid Khodabandehlou and M.  ... 
arXiv:1803.07200v7 fatcat:erwtur3dxjehfgpaq3ocuqsrna

A Generic Neural Network Approach For Constraint Satisfaction Problems [chapter]

E. P. K. Tsang, C. J. Wang
1992 Perspectives in Neural Computing  
This paper describes a neural network approach for solving CSPs which aims at providing prompt responses.  ...  The Constraint Satisfaction Problem (CSP) is a mathematical abstraction of the problems in many AI application domains.  ...  Acknowledgment The authors have benefited from discussions with Dr J. Ford, Dr J. Reynold and Kar-Lik Wong in this research. Jenny Emby has greatly improved the readability of this paper.  ... 
doi:10.1007/978-1-4471-2003-2_2 fatcat:ejh53jd3hjeq5ak36iyv3xuiwq

A Coupled Transiently Chaotic Neural Network Approach for Scheduling Identical Parallel Machines with Sequence Dependent Setup Times

Aiqing Yu, Xingsheng Gu, Bin Jiao
2008 IFAC Proceedings Volumes  
A mixed-integer programming formulation of this problem is presented. And a neural computation architecture based on a Coupled Transiently Chaotic Neural Network is introduced to construct the model.  ...  The transiently chaotic dynamics are defined after the energy function is constructed by a penalty function approach.  ...  The Energy Function for IPMSP Solving an optimization problem with constraints satisfaction requires selecting an appropriate representation of the problem, and choosing the appropriate weights for the  ... 
doi:10.3182/20080706-5-kr-1001.02519 fatcat:y6keehev3vgmpkrmfca4nsa254

FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimize [article]

Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How
2021 arXiv   pre-print
The constraint-satisfaction is achieved via projection onto a polytope formulated by multiple linear inequality constraints, which can be solved analytically with our newly designed metric.  ...  To address this, we propose to learn a generic deep neural network (DNN)-based optimizer to optimize the objective while satisfying the linear constraints.  ...  APPENDIX The hyperparameters for our method and the baselines can be found in the following table, where "NN" and "lr" stand for "neural network" and "learning rate", respectively.  ... 
arXiv:2006.11419v4 fatcat:x3qzrb2wtve6hoplr76c6uhzii

Accessing feasible space in a generalized job shop scheduling problem with the fuzzy processing times: a fuzzy-neural approach

R Tavakkoli-Moghaddam, N Safaei, M M O Kah
2008 Journal of the Operational Research Society  
fuzzy-neural approach for constraint satisfaction of a generalized job shop scheduling problem (GJSSP) fuzzy processing times.  ...  Willems and Rooda (1994) and Willems and Brandts (1995) first proposed a constraint satisfaction neural network for solving traditional job shop scheduling problems with no free operations.  ... 
doi:10.1057/palgrave.jors.2602351 fatcat:yeycbxs4oraclav7wgaa7jy6r4
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