Effective learning hyper-heuristics for the course timetabling problem

Course timetabling is an important and recurring administrative activity in most educational institutions. This article combines a general modeling methodology with effective learning hyper-heuristics to solve this problem. The proposed hyper-heuristics are based on an iterated local search procedure that autonomously combines a set of move operators. Two types of learning for operator selection are contrasted: a static (offline) approach, with a clear distinction between training and execution phases; and a dynamic approach that learns on the fly. The resulting algorithms are tested over the set of real-world instances collected by the first and second International Timetabling competitions. The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms

5-(Biphenyl-4-yl)-3-(3-meth­oxy­benzyl­idene)furan-2(3H)-one

In the title compound, C24H18O3, the dihedral angles between the mean planes of the five-membered furan ring and the meth­oxy-substituted benzene and the adjacent and outer biphenyl benzene rings are 2.43 (7), 4.48 (7) and 30.47 (8)°, respectively. The crystal packing is stabilized by weak C—H⋯O and C—H⋯π inter­molecular hydrogen bonds and π–π stacking inter­actions [centroid–centroid distances = 3.8752 (8) and 3.8331 (8) Å]

Public deliberation as a teaching andragogy: Implications for adult student learning from a doctoral higher education policy course

Public deliberation provides an inclusive and robust mechanism for making shared decisions in community and political settings; however, its application to teaching and learning remains underutilized (McMillan & Harriger, 2007). This manuscript reports on a case study of the use of public deliberation as a teaching andragogy in a graduate level course in higher education policy, which showed that public deliberation creates greater ownership of the course, fosters critical thinking and student agency, and implicates taking action

Iterated local search using an add and delete hyper- heuristic for university course timetabling

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach

Seduced by System: Edmund Burke's Aesthetic Embrace of Adam Smith's Philosophy

No abstract available

Actionable, Pathogenic Incidental Findings in 1,000 Participants’ Exomes

The incorporation of genomics into medicine is stimulating interest on the return of incidental findings (IFs) from exome and genome sequencing. However, no large-scale study has yet estimated the number of expected actionable findings per individual; therefore, we classified actionable pathogenic single-nucleotide variants in 500 European- and 500 African-descent participants randomly selected from the National Heart, Lung, and Blood Institute Exome Sequencing Project. The 1,000 individuals were screened for variants in 114 genes selected by an expert panel for their association with medically actionable genetic conditions possibly undiagnosed in adults. Among the 1,000 participants, 585 instances of 239 unique variants were identified as disease causing in the Human Gene Mutation Database (HGMD). The primary literature supporting the variants’ pathogenicity was reviewed. Of the identified IFs, only 16 unique autosomal-dominant variants in 17 individuals were assessed to be pathogenic or likely pathogenic, and one participant had two pathogenic variants for an autosomal-recessive disease. Furthermore, one pathogenic and four likely pathogenic variants not listed as disease causing in HGMD were identified. These data can provide an estimate of the frequency (∼3.4% for European descent and ∼1.2% for African descent) of the high-penetrance actionable pathogenic or likely pathogenic variants in adults. The 23 participants with pathogenic or likely pathogenic variants were disproportionately of European (17) versus African (6) descent. The process of classifying these variants underscores the need for a more comprehensive and diverse centralized resource to provide curated information on pathogenicity for clinical use to minimize health disparities in genomic medicine