scholarly journals Evolving binary constraint satisfaction problem instances that are difficult to solve

2004 ◽  
Author(s):  
J.I. van Hemert
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problem ◽  
Binary Constraint ◽  
Problem Instances

scholarly journals Variable Elimination in Binary CSPs (Extended Abstract)

2020 ◽  
Author(s):  
Martin C. Cooper ◽  
Achref El Mouelhi ◽  
Cyril Terrioux
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problem ◽  
Benchmark Problems ◽  
Variable Elimination ◽  
Problem Instances

We investigate rules which allow variable elimination in binary CSP (constraint satisfaction problem) instances while conserving satisfiability. We propose new rules and compare them, both theoretically and experimentally. We give optimised algorithms to apply these rules and show that each defines a novel tractable class. Using our variable-elimination rules in preprocessing allowed us to solve more benchmark problems than without.

scholarly journals Variable Elimination in Binary CSPs

2019 ◽  
Vol 66 ◽  
pp. 589-624
Author(s):  
Martin C. Cooper ◽  
Achref El Mouelhi ◽  
Cyril Terrioux
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problem ◽  
Benchmark Problems ◽  
Study Variable ◽  
Variable Elimination ◽  
Problem Instances ◽  
Forbidden Patterns

We investigate rules which allow variable elimination in binary CSP (constraint satisfaction problem) instances while conserving satisfiability. We study variable-elimination rules based on the language of forbidden patterns enriched with counting and quantification over variables and values. We propose new rules and compare them, both theoretically and experimentally. We give optimised algorithms to apply these rules and show that each define a novel tractable class. Using our variable-elimination rules in preprocessing allowed us to solve more benchmark problems than without.

scholarly journals Optimal haplotype reconstruction in half-sib families

10.29007/rpn1 ◽  
2018 ◽  
Author(s):  
Aurélie Favier ◽  
Jean-Michel Elsen ◽  
Simon de Givry ◽  
Andrés Legarra
Keyword(s):  
Constraint Satisfaction ◽  
Marker Assisted Selection ◽  
Genetic Improvement ◽  
Constraint Satisfaction Problem ◽  
Soft Constraints ◽  
Haplotype Reconstruction ◽  
Binary Constraint ◽  
Sib Families

In the goal of genetic improvement of livestock by marker assisted selection, we aim at reconstructing the haplotypes of sires from their offspring. We reformulate this problem into a weighted binary constraint satisfaction problem with only equality and disequality soft constraints. Our results show these problems have a small treewidth and can be solved optimally, improving haplotype reconstruction compared to previous approaches especially for small families (<10 descendants).

Evolving Combinatorial Problem Instances That Are Difficult to Solve

2006 ◽  
Vol 14 (4) ◽  
pp. 433-462 ◽  
Author(s):  
Jano I. van Hemert
Keyword(s):  
Evolutionary Computation ◽  
Structural Properties ◽  
Constraint Satisfaction ◽  
Travelling Salesman Problem ◽  
Combinatorial Problem ◽  
Boolean Satisfiability ◽  
Combinatorial Optimisation ◽  
Travelling Salesman ◽  
Binary Constraint ◽  
Problem Instances

This paper demonstrates how evolutionary computation can be used to acquire difficult to solve combinatorial problem instances. As a result of this technique, the corresponding algorithms used to solve these instances are stress-tested. The technique is applied in three important domains of combinatorial optimisation, binary constraint satisfaction, Boolean satisfiability, and the travelling salesman problem. The problem instances acquired through this technique are more difficult than the ones found in popular benchmarks. In this paper, these evolved instances are analysed with the aim to explain their difficulty in terms of structural properties, thereby exposing the weaknesses of corresponding algorithms.

scholarly journals Minimizing Cost Travel in Multimodal Transport Using Advanced Relation Transitive Closure

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Rachid Oucheikh ◽  
Ismail Berrada ◽  
Lahcen Omari
Keyword(s):  
Binary Relation ◽  
Constraint Satisfaction ◽  
Operations Research ◽  
Transitive Closure ◽  
Constraint Satisfaction Problem ◽  
Cost Optimization ◽  
Binary Relations ◽  
Mathematical Background ◽  
Multimodal Transport ◽  
Binary Constraint

The optimization computation is an essential transversal branch of operations research which is primordial in many technical fields: transport, finance, networks, energy, learning, etc. In fact, it aims to minimize the resource consumption and maximize the generated profits. This work provides a new method for cost optimization which can be applied either on path optimization for graphs or on binary constraint reduction for Constraint Satisfaction Problem (CSP). It is about the computing of the “transitive closure of a given binary relation with respect to a property.” Thus, this paper introduces the mathematical background for the transitive closure of binary relations. Then, it gives the algorithms for computing the closure of a binary relation according to another one. The elaborated algorithms are shown to be polynomial. Since this technique is of great interest, we show its applications in some important industrial fields.

scholarly journals A Variable Depth Search Algorithm for Binary Constraint Satisfaction Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
N. Bouhmala
Keyword(s):  
Constraint Satisfaction ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Wide Spectrum ◽  
Constraint Satisfaction Problem ◽  
Practical Interest ◽  
Constraint Satisfaction Problems ◽  
Variable Depth ◽  
The Past ◽  
Binary Constraint

The constraint satisfaction problem (CSP) is a popular used paradigm to model a wide spectrum of optimization problems in artificial intelligence. This paper presents a fast metaheuristic for solving binary constraint satisfaction problems. The method can be classified as a variable depth search metaheuristic combining a greedy local search using a self-adaptive weighting strategy on the constraint weights. Several metaheuristics have been developed in the past using various penalty weight mechanisms on the constraints. What distinguishes the proposed metaheuristic from those developed in the past is the update ofkvariables during each iteration when moving from one assignment of values to another. The benchmark is based on hard random constraint satisfaction problems enjoying several features that make them of a great theoretical and practical interest. The results show that the proposed metaheuristic is capable of solving hard unsolved problems that still remain a challenge for both complete and incomplete methods. In addition, the proposed metaheuristic is remarkably faster than all existing solvers when tested on previously solved instances. Finally, its distinctive feature contrary to other metaheuristics is the absence of parameter tuning making it highly suitable in practical scenarios.

Sign in / Sign up

Export Citation Format

Share Document