What should Japanese doctors do when asked by a patient for active voluntary euthanasia, when the family wants aggressive treatment to continue? In this paper, we present the results of a questionnaire survey of 366 Japanese doctors, who were asked how they would act in a hypothetical situation of this kind, and how they would justify their decision, 23% of respondents said they would act on the patient’s wishes, and provided reasons for their view; 54% said they would not practice (...) VE, either because they were opposed to VE as such, or because they believed that the wishes of the patient’s family should be respected.Analysis of these responses yielded the following results: Doctors willing to respect the patient’s wishes defended their decision by highlighting the significance of patient autonomy and the patient’s exclusive ownership of his or her life; doctors unwilling to act on the patient’s wishes fell into two broad categories — those who based their reasoning on the family’s objections, and those who provided other reasons for refusing VE. Respondents who said they would not comply with the patient’s wishes because of family objections provided the following kinds of rationale: doctors have serious responsibilities not only to the patient, but also to the patient’s family; the importance of the family-doctor relationship; fear of lawsuits for murder and related criminal offences; the need for agreement among all those affected by the decision, and the belief that the patient’s life is not his or her own, but the family’s. Respondents who gave non-family centred reasons for not complying with the patient’s wishes pointed to values such as the sanctity of life, or the importance of a natural deathIn the remainder of this paper, we discuss the implications of a family-centred approach to VE. (shrink)

The results of evolutionary algorithms depend on population diversity that normally decreases by increasing the selection pressure from generation to generation. Usually, this can lead the evolution process to get stuck in local optima. This study is focused on mechanisms to avoid this undesired phenomenon by introducing parallel self-adapted differential evolution that decomposes a monolithic population into more variable-sized sub-populations and combining this with the characteristics of evolutionary multi-agent systems into a hybrid algorithm. The proposed hybrid algorithm operates with individuals (...) having some characteristics of agents, e.g. they act autonomously by selecting actions, with which they affect the state of the environment. Additionally, this algorithm incorporates two additional mechanisms: ageing and adaptive population growth, which help the individuals by decision-making. The proposed parallel differential evolution was applied to the CEC’18 benchmark function suite, while the produced results were compared with some traditional stochastic nature-inspired population-based and state-of-the-art algorithms. (shrink)