research-article

Guiding Evolutionary Search with Association Rules for Solving Weighted CSPs

Authors:

Madalina Raschip,

Cornelius Croitoru,

Kilian StoffelAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 481 - 488

https://doi.org/10.1145/2739480.2754757

Published: 11 July 2015 Publication History

Abstract

Weighted constraint satisfaction problems are difficult optimization problems that could model applications from various domains. Evolutionary algorithms are not the first option for solving such type of problems. In this work, the evolutionary algorithm uses the information extracted from the previous best solutions to guide the search in the next iterations. After the archive of previous best solutions has been sufficiently (re)filled, a data mining module is called to find association rules between variables and values. The generated rules are used to improve further the search process. Different methods of applying the association rules are investigated. Computational experiments are done on academic and real-world problem instances. The obtained results validate the approach and show that it is competitive with existing approaches in literature.

References

[1]

R. Agrawal and S. Ramakrishnan. Fast algorithms for mining association rules. In Proceedings of 20th International Conference of Very Large Data Bases (VLDB), volume 1215, pages 487--499, 1994.

Digital Library

[2]

S. Baluja. Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report Carnegie Mellon University-CS-94--163, Pittsburgh, PA, USA, 1994.

[3]

J. Chia, C. Goh, K. Tan, and V. Shim. Memetic informed evolutionary optimization via data mining. Memetic Computing, 3(2):73--87, 2011.

[4]

B. Craenen, A. Eiben, and J. van Hemert. Comparing evolutionary algorithms on binary constraint satisfaction problems. IEEE Transactions on Evolutionary Computation, 7(5):424--444, 2003.

Digital Library

[5]

S. de Givry, M. Zytnicki, F. Heras, and J. Larrosa. Existential arc consistency: Getting closer to full arc consistency in weighted csps. In Proceedings of the 19th International Joint Conference on Artificial Intelligence, IJCAI'05, pages 84--89, 2005.

Digital Library

[6]

R. Dechter. Constraint processing. Morgan Kaufmann, 2003.

Digital Library

[7]

A. A. Freitas. A survey of evolutionary algorithms for data mining and knowledge discovery. In Advances in Evolutionary Computing, pages 819--845. 2003.

Digital Library

[8]

J. E. Gallardo, C. Cotta, and A. J. Fernández. Solving weighted constraint satisfaction problems with memetic/exact hybrid algorithms. Journal of Artificial Intelligence Research, 35(1):533--555, 2009.

Digital Library

[9]

N. Gharbi, F. Hemery, C. Lecoutre, and O. Roussel. Sliced table constraints: Combining compression and tabular reduction. In Integration of AI and OR Techniques in Constraint Programming, pages 120--135. Springer, 2014.

[10]

D. E. Goldberg. The design of innovation: Lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Norwell, MA, USA, 2002.

Digital Library

[11]

J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proceedings of ACM SIGMOD International Conference on Management of Data, volume 29, pages 1--12, 2000.

Digital Library

[12]

M. Ionita, C. Croitoru, and M. Breaban. Incorporating inference into evolutionary algorithms for max-csp. In Hybrid Metaheuristics, pages 139--149. Springer, 2006.

Digital Library

[13]

L. Jourdan, C. Dhaenens, and E.-G. Talbi. Using datamining techniques to help metaheuristics: A short survey. In Hybrid Metaheuristics, pages 57--69. Springer, 2006.

Digital Library

[14]

K. Kask and R. Dechter. Gsat and local consistency. In Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 1, IJCAI'95, pages 616--622, 1995.

Digital Library

[15]

J. Larrosa and R. Dechter. Boosting search with variable elimination in constraint optimization and constraint satisfaction problems. Constraints, 8(3):303--326, 2003.

Digital Library

[16]

S. Loudni and P. Boizumault. Combining vns with constraint programming for solving anytime optimization problems. European Journal of Operational Research, 191(3):705 -- 735, 2008.

[17]

R. Michalski. Learnable evolution model: Evolutionary processes guided by machine learning. Machine Learning, 38(1--2):9--40, 2000.

Digital Library

[18]

S. Minton, M. D. Johnston, A. B. Philips, and P. Laird. Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 58(1--3):161 -- 205, 1992.

Digital Library

[19]

P. Morris. The breakout method for escaping local minima. In Proceedings of the 11th National Conference on Artificial Intelligence, pages 40--45, 1993.

Digital Library

[20]

K. Nonobe and T. Ibaraki. An improved tabu search method for the weighted constraint satisfaction problem. Infor-Information Systems and Operational Research, 39(2):131--151, 2001.

[21]

L. D. Raedt, S. Nijssen, B. O'Sullivan, and P. V. Hentenryck. Constraint programming meets machine learning and data mining. Dagstuhl Reports, 1(5):61--83, 2011.

[22]

M. Sanchez et al. Max-csp competition 2008: toulbar2 solver description. Proceedings of the 3rd International CSP Solver Competition, pages 63--70, 2008.

[23]

H. Santos, L. Ochi, E. Marinho, and L. Drummond. Combining an evolutionary algorithm with data mining to solve a single-vehicle routing problem. Neurocomputing, 70(1):70--77, 2006.

[24]

T. Schiex, H. Fargier, and G. Verfaillie. Valued constraint satisfaction problems: Hard and easy problems. In Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 1, IJCAI'95, pages 631--637, 1995.

Digital Library

[25]

C. Solnon. Ants can solve constraint satisfaction problems. IEEE Transactions on Evolutionary Computation, 6(4):347--357, 2002.

Digital Library

Cited By

Richthammer VScheinert TGlaß M(2020)Data Mining in System-Level Design Space Exploration of Embedded SystemsEmbedded Computer Systems: Architectures, Modeling, and Simulation10.1007/978-3-030-60939-9_4(52-66)Online publication date: 5-Jul-2020
https://dl.acm.org/doi/10.1007/978-3-030-60939-9_4

Index Terms

Guiding Evolutionary Search with Association Rules for Solving Weighted CSPs
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

Recommendations

Discovering association rules change from large databases
AICI'11: Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I

Discovering association rules and association rules change (ARC) from existing large databases is an important problem. This paper presents an approach based on multi-hash chain structures to mine association rules change from large database with ...
Mining Negatives Association Rules Using Constraints

Pattern discovery techniques, such as association rule discovery is one of the fundamental problem in data mining. Usually the task is limited to positive rules of the form X Y when X and Y are susbets of items. To enlarge the knowledge discovery from ...
Mining weighted association rules

Association rules are useful for determining correlations between items and have applications in marketing, financial and retail sectors. Lots of algorithms have been proposed for finding the association rules in databases. Most of these algorithms ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Sciex

Conference

GECCO '15

Sponsor:

SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

Acceptance Rates

GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
86
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Richthammer VScheinert TGlaß M(2020)Data Mining in System-Level Design Space Exploration of Embedded SystemsEmbedded Computer Systems: Architectures, Modeling, and Simulation10.1007/978-3-030-60939-9_4(52-66)Online publication date: 5-Jul-2020
https://dl.acm.org/doi/10.1007/978-3-030-60939-9_4

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten