Name
Abstract | ||
|---|---|---|
A simple model shows how a reasonable update scheme for the probability vector by which a hyper-heuristic chooses the next heuristic leads to neglecting useful mutation heuristics. Empirical evidence supports this on the MAXSAT, TRAVELINGSALESMAN, PERMUTATION-FLOWSHOP and VEHICLEROUTINGPROBLEM problems. A new approach to hyper-heuristics is proposed that addresses this problem by modeling and learning hyper-heuristics by means of a hidden Markov Model. Experiments show that this is a feasible and promising approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-19084-6_7 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Mathematical optimization,Markov process,Maximum-entropy Markov model,Forward algorithm,Markov model,Computer science,Hyper-heuristic,Variable-order Markov model,Hidden Markov model,Hidden semi-Markov model | Conference | 8994 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Willem Van Onsem | 1 | 0 | 0.34 |
| bart demoen | 2 | 956 | 77.58 |
| Patrick De Causmaecker | 3 | 11 | 1.37 |