In this paper, we discuss the idea of incorporating preference information into evolutionary multi-objective optimization and propose a preference-based evolutionary approach that can be used as an integral part of an interactive algorithm. One algorithm is proposed in the paper. At each iteration, the decision maker is asked to give preference information in terms of his or her reference point consisting of desirable aspiration levels for objective functions. The information is used in an evolutionary algorithm to generate a new population by combining the fitness function and an achievement scalarizing function. In multi-objective optimization, achievement scalarizing functions are widely used to project a given reference point into the Pareto optimal set. In our approach, the next population is thus more concentrated in the area where more preferred alternatives are assumed to lie and the whole Pareto optimal set does not have to be generated with equal accuracy. The approach is demonstrated by numerical examples.

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Efficiency has become one of the main concerns in evolutionary multiobjective optimization during recent years. One of the possible alternatives to achieve a faster convergence is to use a relaxed form of Pareto dominance that allows us to regulate the granularity of the approximation of the Pareto front that we wish to achieve. One such relaxed forms of Pareto dominance that has become popular in the last few years is ε-dominance, which has been mainly used as an archiving strategy in some multiobjective evolutionary algorithms. Despite its advantages, ε-dominance has some limitations. In this paper, we propose a mechanism that can be seen as a variant of ε-dominance, which we call Pareto-adaptive ε-dominance (paε-dominance). Our proposed approach tries to overcome the main limitation of ε-dominance: the loss of several nondominated solutions from the hypergrid adopted in the archive because of the way in which solutions are selected within each box.

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BackgroundAround the world, different models of paediatric palliative care have responded to the unique needs of children with life shortening conditions. However, research confirming their utility and impact is still lacking. This study compared patient-related outcomes and healthcare expenditures between those who received home-based paediatric palliative care and standard care. The quality of life and caregiver burden for patients receiving home-based paediatric palliative care were also tracked over the first year of enrolment to evaluate the service’s longitudinal impact.MethodA structured impact and cost evaluation of Singapore-based HCA Hospice Care’s Star PALS (Paediatric Advance Life Support) programme was conducted over a three-year period, employing both retrospective and prospective designs with two patient groups.ResultsCompared to the control group (n = 67), patients receiving home-based paediatric palliative care (n = 71) spent more time at home than in hospital in the last year of life by 52 days (OR = 52.30, 95% CI: 25.44–79.17) with at least two fewer hospital admissions (OR = 2.46, 95% CI: 0.43–4.48); and were five times more likely to have an advance care plan formulated (OR = 5.51, 95% CI: 1.55–19.67). Medical costs incurred by this group were also considerably lower (by up to 87%). Moreover, both patients’ quality of life (in terms of pain and emotion), and caregiver burden showed improvement within the first year of enrolment into the programme.DiscussionOur findings suggest that home-based paediatric palliative care brings improved resource utilization and cost-savings for both patients and healthcare providers. More importantly, the lives of patients and their caregivers have improved, with terminally ill children and their caregivers being able to spend more quality time at home at the final stretch of the disease.ConclusionsThe benefits of a community paediatric palliative care programme have been validated. Study findings can become key drivers when engaging service commissioners or even policy makers in appropriate settings.