Name
Abstract | ||
|---|---|---|
Solving ill-posed continuous, global optimization problems remains challenging. For example, there are no well-established methods for handling objective insensitivity in the neighborhood of solutions, which appears in many important applications, e.g., in non-invasive tumor tissue diagnosis or geophysical exploration. The paper presents a complex metaheuristic that identifies regions of objective function's insensitivity (plateaus). The strategy is composed of a multi-deme hierarchic memetic strategy coupled with random sample clustering, cluster integration, and special kind of multiwinner selection that allows to breed the demes and cover each plateau separately. We test the method on benchmarks with multiple non-convex plateaus and evaluate how well the plateaus are covered. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-55849-3_18 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Ill-posed global optimization problems,New tournament-like selection,Fitness insensitivity | Mathematical optimization,Tissue diagnosis,Global optimization,Computer science,Sampling (statistics),Cluster analysis,Metaheuristic,Global optimization problem | Conference |
Volume | ISSN | Citations |
10199 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jakub Sawicki | 1 | 20 | 3.68 |
| Maciej Smołka | 2 | 107 | 13.60 |
| Marcin Los | 3 | 18 | 4.56 |
| Robert Schaefer | 4 | 101 | 10.99 |
| Piotr Faliszewski | 5 | 1395 | 94.15 |