Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

In this paper, we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different graph colouring heuristics that construct solutions step by step. Based on these sequences, we statistically analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the search effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate high quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme on developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods.

An Investigation of Fuzzy Multiple Heuristic Orderings in the Construction of University Examination Timetables

In this paper, we present an investigation into using fuzzy methodologies to guide the construction of high quality feasible examination timetabling solutions. The provision of automated solutions to the examination timetabling problem is achieved through a combination of construction and improvement. The enhancement of solutions through the use of techniques such as metaheuristics is, in some cases, dependent on the quality of the solution obtained during the construction process. With a few notable exceptions, recent research has concentrated on the improvement of solutions as opposed to focusing on investigating the ‘best’ approaches to the construction phase. Addressing this issue, our approach is based on combining multiple criteria in deciding on how the construction phase should proceed. Fuzzy methods were used to combine three single construction heuristics into three different pair wise combinations of heuristics in order to guide the order in which exams were selected to be inserted into the timetable solution. In order to investigate the approach, we compared the performance of the various heuristic approaches with respect to a number of important criteria (overall cost penalty, number of skipped exams, number of iterations of a rescheduling procedure required and computational time) on twelve well-known benchmark problems. We demonstrate that the fuzzy combination of heuristics allows high quality solutions to be constructed. On one of the twelve problems we obtained lower penalty than any previously published constructive method and for all twelve we obtained lower penalty than when any of the single heuristics were used alone. Furthermore, we demonstrate that the fuzzy approach used less backtracking when constructing solutions than any of the single heuristics. We conclude that this novel fuzzy approach is a highly effective method for heuristically constructing solutions and, as such, has particular relevance to real-world situations in which the construction of feasible solutions is often a difficult task in its own right.

The construction of a whole-cell biosensor for phosphonoacetate, based on the LysR-like transcriptional regulator PhnR from Pseudomonas fluorescens 23F

The phnA gene that encodes the carbon-phosphorus bond cleavage enzyme phosphonoacetate hydrolase is widely distributed in the environment, suggesting that its phosphonate substrate may play a significant role in biogeochemical phosphorus cycling. Surprisingly, however, no biogenic origin for phosphonoacetate has yet been established. To facilitate the search for its natural source we have constructed a whole-cell phosphonoacetate biosensor. The gene encoding the LysR-type transcriptional activator PhnR, which controls expression of the phosphonoacetate degradative operon in Pseudomonas fluorescens 23F, was inserted in the broad-host-range promoter probe vector pPROBE-NT, together with the promoter region of the structural genes. Cells of Escherichia coli DH5a that contained the resultant construct, pPANT3, exhibited phosphonoacetate-dependent green fluorescent protein fluorescence in response to threshold concentrations of as little as 0.5 µM phosphonoacetate, some 100 times lower than the detection limit of currently available non-biological analytical methods; the pPANT3 biosensor construct in Pseudomonas putida KT2440 was less sensitive, although with shorter response times. From a range of other phosphonates and phosphonoacetate analogues tested, only phosphonoacetaldehyde and arsonoacetate induced green fluorescent protein fluorescence in the E. coli DH5a (pPANT3) biosensor, although at much-reduced sensitivities (50 µM phosphonoacetaldehyde and 500 µM arsonoacetate).

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

The construction of images of people and place: Labelling Liverpool and stereotyping Scousers.

On 28th August 1207, King John created the Borough of Liverpool by granting its first charter. During the ensuing 800 years Liverpool has experienced a complex and changing social, economic and political history resulting in powerful images of the city and its people. This paper examines the labelling of Liverpool and stereotypes of Scousers. It explains how historical and contemporary events, and their coverage in various arms of the media, construct social and spatial imaginations of the city. This involves a more systematic contribution to the how and why dimensions of negative place imagery and social stereotypes, and enhances our understanding of the processes and issues affecting our interpretations of people and place. The analysis is both historical and contemporaneous in teasing out how previous and current events shape the perceptions of insiders and outsiders. This paper reveals that despite concerted efforts to re-brand Liverpool the city continues to face difficult challenges with ongoing bad publicity and negative place imagery.