TY - GEN
T1 - Extremal optimisation and bin packing
AU - Hendtlass, Tim
AU - Randall, Marcus
PY - 2008
Y1 - 2008
N2 - Extremal Optimisation (EO) is a fairly new entrant into the realms of stochastic based optimisation techniques. Its behaviour differs from other more common algorithms as it alters a poorly performing part of the one solution used without regard to the effect this will have on the quality of the solution. While this means that its performance on assignment problems may be poor if used on its own, this same 'failing' makes it a very suitable base for a meta-heuristic. An analysis of the performance of naive EO on the classic bin packing problem is performed in this paper. Results are also presented that show that the same naive EO can be used in a meta-heuristic that performs very well.
AB - Extremal Optimisation (EO) is a fairly new entrant into the realms of stochastic based optimisation techniques. Its behaviour differs from other more common algorithms as it alters a poorly performing part of the one solution used without regard to the effect this will have on the quality of the solution. While this means that its performance on assignment problems may be poor if used on its own, this same 'failing' makes it a very suitable base for a meta-heuristic. An analysis of the performance of naive EO on the classic bin packing problem is performed in this paper. Results are also presented that show that the same naive EO can be used in a meta-heuristic that performs very well.
UR - http://www.scopus.com/inward/record.url?scp=58349120897&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-89694-4_23
DO - 10.1007/978-3-540-89694-4_23
M3 - Conference contribution
AN - SCOPUS:58349120897
SN - 3540896937
SN - 9783540896937
VL - 5361 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 220
EP - 228
BT - Simulated Evolution and Learning - 7th International Conference, SEAL 2008, Proceedings
T2 - 7th International Conference on Simulated Evolution and Learning, SEAL 2008
Y2 - 7 December 2008 through 10 December 2008
ER -