Iterated local search and constructive heuristics for error correcting code design

International Journal of Innovative Computing and Applications

Volumn 1, Issue 1, 2007, Pages 14-22

  Blum, Christian ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

constructive heuristics; ECCs; error correcting code design; iterated local search; non linear binary block codes

Indexed keywords

EID: 70349861309     PISSN: 1751648X     EISSN: 17516498     Source Type: Journal    
DOI: 10.1504/IJICA.2007.013398     Document Type: Article

References (23)

1. Agrell, E., Vardy, A. and Zeger, K. (2001) ‘A table of upper bounds for binary codes’, IEEE Transactions on Information Theory.
2. Agrell, E. (2006) Available at: http://www.s2.chalmers.se/~agrell/bounds/unr.html. Visited in October.
3. Alba, E. and Chicano, J.F. (2004) ‘Solving the error correcting code problem with parallel hybrid heuristics’, in H. Haddad, A. Omicini, R.L. Wainwright and L.M. Liebrock (Eds). Proceedings of the 2004ACM Symposium on Applied Computing (SAC 2004), ACM Press, pp.985–989.
4. Blum, C., Blesa, M. and Roli, A. (2005) ‘Combining ILS with an effective constructive heuristic for the application to error correcting code design’, Proceedings ofthe Sixth Metaheuristics International Conference, pp.114–119.
5. Blum, C. and Roli, A. (2003) ‘Metaheuristics in combinatorial optimization: overview and conceptual comparison’, ACM Computing Surveys, Vol. 35, No. 3, pp.268–308.
6. Brouwer, A.E. and Verhoeff, T. (1993) ‘An updated table of minimum-distance bounds for binary linear codes’, IEEE Transactions on Information Theory, Vol. 39, No. 2, pp.662–677.
7. Chen, H., Flann, N.S. and Watson, D.W. (1998) ‘Parallel genetic simulated annealing: a massively parallel SIMD algorithm’, IEEE Transactions on Parallel and Distributed Systems, Vol. 9, No. 2, pp.126–136.
8. Cotta, C. (2004) ‘Scatter search and memetic approaches to the error correcting code problem’, in J. Gottlieb and G.R. Raidl (Eds). Proceedings of the Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004), Number 3004 in Lecture Notes in Computer Science, Berlin, Germany: Springer Verlag, pp.51–61.
9. Dontas, K. and De Jong, K. (1990) ‘Discovery of maximal distance codes using genetic algorithms’, Proceedings of the Second International IEEE Conference on Tools for Artificial Intelligence, Los Alamitos, CA: IEEE Computer Society Press, pp.905–911.
10. Gamal, A., Hemachandra, L., Shaperling, I. and Wei, V. (1987) ‘Using simulated annealing to design good codes’, IEEE Transactions on Information Theory, Vol. 33, pp.116–123.
11. Glover, F. and Kochenberger, G. (2002) Handbook of Metaheuristics, Norwell, MA: Kluwer Academic Publishers.
12. Hoos, H.H. and Stützle, T. (2005) Stochastic Local Search. Foundations and Applications, San Francisco, CA: Morgan Kaufmann Publishers.
13. Key, J.D. (1999) Applied Mathematical Modeling: A Multidisciplinary Approach, Chapter Some Error-Correcting Codes and their Applications, Boca Raton, FL: CRC Press.
14. Lourenço, H.R., Martin, O. and Stützle, T. (2002) ‘Iterated local search’, in F. Glover and G. Kochenberger (Eds). Handbook of Metaheuristics, Volume 57 of International Series in Operations Research and Management Science, Norwell, MA: Kluwer Academic Publishers, pp.321–353.
15. MacWilliams, FJ. and Sloane, N.J.A. (1977) The Theory of Error-Correcting Codes, North-Holland, NY.
16. Morelos-Zaragoza, R.H. (2004) The Art of Error Correcting Coding, Wiley.
17. Sloane, N.J.A. (2006) Available at: http://www.research.att.com/njas/codes/Andw/. Visited on October.
18. Stützle, T. (1999) ‘Local search algorithms for combinatorial problems – analysis, algorithms and new applications’, DISKI – Dissertationen zur Künstliken Intelligenz. infix, Sankt Augustin, Germany.
19. Verhoeff, T. (1987) ‘An updated table of minimum-distance bounds for binary linear codes’, IEEE Transactions on Information Theory, Vol. 33, No. 5, pp.665–680.
20. For an overview on metaheuristics see Blum and Roli (2003) and Glover and Kochenberger (2002).
21. ILS is one of the principal metaheuristic methods; see, for example Hoos and Stützle (2005), Lourenço et al. (2002) and Stützle (1999).
22. Citation from Alba and Chicano (2004): ‘In this paper we solve the problem for a 24-word code, with 12-bit length code-words (M = 24, n = 12) as in Chen et al. (1998). This instance of the problem has been largely addressed in the literature because of its difficulty’.
23. Note that this problem dependent limit was chosen after tuning by hand.