Evolutionary Time-Use Optimization for Improving Children's Health Outcomes

TL;DR

This paper demonstrates how evolutionary algorithms can optimize children's weekly time use to improve multiple health outcomes, revealing trade-offs and potential strategies for better health management.

Contribution

It introduces a novel application of evolutionary multi-objective optimization to design time-use plans for children's health based on real cohort data.

Findings

Optimized time plans can improve specific health outcomes.

Multi-objective optimization reveals trade-offs between health goals.

Evolutionary algorithms effectively generate viable weekly schedules.

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.