Name
Abstract | ||
|---|---|---|
Multifactorial optimization MFO is a recently proposed paradigm for evolutionary multitasking that is inspired by the possibility of harnessing underlying synergies between outwardly unrelated optimization problems through the process of implicit genetic transfer. In contrast to traditional single-objective and multi-objective optimization, which consider only a single problem in one optimization run, MFO aims at solving multiple optimization problems simultaneously. Through comprehensive empirical study, MFO has demonstrated notable performance on a variety of complex optimization problems. In this paper, we take a step towards better understanding the means by which MFO leads to the observed performance improvement. In particular, since a genetic and b cultural transmission across generations form the crux of the proposed evolutionary multitasking engine, we focus on how their interaction i.e., gene-culture interaction affects the overall efficacy of this novel paradigm. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-28270-1_5 | ACALCI |
DocType | Volume | ISSN |
Conference | 9592 | 0302-9743 |
Citations | PageRank | References |
1 | 0.39 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingshui Da | 1 | 15 | 2.00 |
| Abhishek Gupta | 2 | 351 | 20.59 |
| Yew-Soon Ong | 3 | 4205 | 224.11 |
| Liang Feng | 4 | 601 | 48.54 |