Haiku - a Scala combinator toolkit for semi-automated composition of metaheuristics

There is an emerging trend towards the automated design of metaheuristics at the software component level. In principle, metaheuristics have a relatively clean decomposition, where well-known frameworks such as ILS and EA are parametrised by variant components for acceptance, perturbation etc. Automated generation of these frameworks is not so simple in practice, since the coupling between components may be implementation specific. Compositionality is the ability to freely express a space of designs ‘bottom up’ in terms of elementary components: previous work in this area has used combinators, a modular and functional approach to componentisation arising from foundational Computer Science. In this article, we describeHaiku, a combinator tool-kit written in the Scala language, which builds upon previous work to further automate the process by automatically composing the external dependencies of components. We provide examples of use and give a case study in which a programatically-generated heuristic is applied to the Travelling Salesman Problem within an Evolutionary Strategies framework

Reduction in inappropriate hospital use based on analysis of the causes

<p>Abstract</p> <p>Background</p> <p>To reduce inappropriate admissions and stays with the application of an improvement cycle in patients admitted to a University Hospital. The secondary objective is to analyze the hospital cost saved by reducing inadequacy after the implementation of measures proposed by the group for improvement.</p> <p>Methods</p> <p>Pre- and post-analysis of a sample of clinical histories studied retrospectively, in which the Appropriateness Evaluation Protocol (AEP) was applied to a representative hospital sample of 1350 clinical histories in two phases. In the first phase the AEP was applied retrospectively to 725 admissions and 1350 stays. The factors associated with inappropriateness were analysed together with the causes, and specific measures were implemented in a bid to reduce inappropriateness. In the second phase the AEP was reapplied to a similar group of clinical histories and the results of the two groups were compared. The cost of inappropriate stays was calculated by cost accounting. Setting: General University Hospital with 426 beds serving a population of 320,000 inhabitants in the centre of Murcia, a city in south-eastern Spain.</p> <p>Results</p> <p>Inappropriate admissions were reduced significantly: 7.4% in the control group and 3.2% in the intervention group. Likewise, inappropriate stays decreased significantly from 24.6% to 10.4%. The cost of inappropriateness in the study sample fell from 147,044 euros to 66,642 euros. The causes of inappropriateness for which corrective measures were adopted were those that showed the most significant decrease.</p> <p>Conclusions</p> <p>It is possible to reduce inadequacy by applying measures based on prior analysis of the situation in each hospital.</p

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018