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The ability of evolutionary processes to innovate and scale up over long periods of time, observed in nature, remains a central mystery in evolutionary biology, and a challenge for algorithm designers to emulate and explain in evolutionary computation (EC). The Major Transitions in Evolution is a compelling theory that explains evolvability through a multi-scale process whereby individuality (and hence selection and variation) is continually revised by the formation of associations between formerly independent entities, a process still not fully explored in EC. Deep Optimisation (DO) is a new type of model-building optimization algorithm (MBOA) that exploits deep learning methods to enable multi-scale optimization. DO uses an autoencoder model to induce a multi-level representation of solutions, capturing the relationships between the lower-level units that contribute to the quality of a solution. Variation and selection are then performed within the induced representations, causing model-informed changes to multiple solution variables simultaneously. Here, we first show that DO has impressive performance compared with other leading MBOAs (and other rival methods) on multiple knapsack problems, a standard combinatorial optimization problem of general interest. Going deeper, we then carry out a detailed investigation to understand the differences between DO and other MBOAs, identifying key problem characteristics where other MBOAs are afflicted by exponential running times, and DO is not. This study serves to concretize our understanding of the Major Transitions theory, and why that leads to evolvability, and also provides a strong motivation for further investigation of deep learning methods in optimization.

Objective: Drink driving is widely recognized as a major road safety problem. In Australia, health promotion messages encourage monitoring the number of standard drinks consumed prior to driving. This pilot research aimed to investigate commuting behavior and blood alcohol concentration (BAC) of diners, including intended drivers, at Sunshine Coast restaurants. Methods: Five hundred and forty-four diners (n = 260 males) consented to participate in a brief interview and to use a breathalyzer device to measure their BAC. Results: Forty percent of participants advised they don't drink and drive (34% of males, 45% of females; 67.25% of Conclusions: Given the proportion of diners who reported they count the number of drinks, or use feelings as a way to gauge BAC, coupled with the considerable proportion who underestimated their BAC, a safer public health message is to avoid driving if you intend to drink. In addition, targeted intervention for experienced drivers (and, arguably, drinkers) appears warranted, as every participant aged less than 21 years who stated he or she would drive home indeed had a zero BAC. Interestingly every female driver who stated she would be driving home also had a legal BAC, suggesting gender-specific intervention.

Untreated hyperthyroidism during pregnancy is associated with a high incidence of maternal and fetal complications. The perinatal nurse needs knowledge of the pathophysiology of this condition to implement a care plan. Antithyroid medications are used to restore the patient's normal thyroid function. Ongoing evaluation of clinical and laboratory data assists the nurse in recognizing the development and implementation of interventions for complications, such as thyroid crisis and heart failure, in the pregnant patient.