Name
Abstract | ||
|---|---|---|
How someone allocates their time is important to their health and well-being. In this paper, we show how evolutionary algorithms can be used to promote health and well-being by optimizing time usage. Based on data from a large population-based child cohort, we design fitness functions to explain health outcomes and introduce constraints for viable time plans. We then investigate the performance of evolutionary algorithms to optimize time use for four individual health outcomes with hypothetical children with different day structures. As the four health outcomes are competing for time allocations, we study how to optimize multiple health outcomes simultaneously in the form of a multi-objective optimization problem. We optimize one-week time-use plans using evolutionary multi-objective algorithms and point out the trade-offs achievable with respect to different health outcomes. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-14721-0_23 | PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XVII, PPSN 2022, PT II |
Keywords | DocType | Volume |
Real-world application, Time-use optimization, Single-objective optimization, Multi-objective optimization | Conference | 13399 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Xie | 1 | 0 | 2.37 |
| Aneta Neumann | 2 | 14 | 12.79 |
| Ty Stanford | 3 | 0 | 0.34 |
| Charlotte Lund Rasmussen | 4 | 0 | 0.34 |
| Dorothea Dumuid | 5 | 0 | 0.34 |
| Frank Neumann | 6 | 1727 | 124.28 |