Name
Abstract | ||
|---|---|---|
ABSTRACTSelection hyper-heuristics have been increasingly and successfully applied to numerical and discrete optimization problems. This paper proposes HHTS, a hyper-heuristic (HH) based on the Thompson Sampling (TS) mechanism to select combinations of low-level heuristics aiming to provide solutions for various continuous single-objective optimization benchmarks. Thompson Sampling is modeled in the present paper as a Beta Bernoulli sampler considering the increase/decrease of diversity among population individuals to measure the success/failure during the search. In the experiments, HHTS (a generic evolutionary algorithm generated by TS) is compared with five well-known evolutionary algorithms. Results indicate that, despite requiring less computational effort, HHTS's performance is similar or better than the other algorithm for most instances and in 50% of the cases it is capable of achieving the global optimum. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3449726.3463140 | Genetic and Evolutionary Computation Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcella Scoczynski Ribeiro Martins | 1 | 0 | 0.34 |
| Diego Oliva | 2 | 0 | 0.34 |
| Erick Rodríguez-Esparza | 3 | 0 | 0.34 |
| Myriam Regattieri Delgado | 4 | 224 | 22.26 |
| Ricardo Lüders | 5 | 24 | 7.23 |
| Mohamed El Yafrani | 6 | 0 | 0.34 |
| Luiz Ledo | 7 | 0 | 0.34 |
| Mohamed E. Abd Elaziz | 8 | 48 | 7.07 |
| Marco Peréz-Cisnero | 9 | 0 | 0.34 |