A service oriented architecture for engineering design

Decision making in engineering design can be effectively addressed by using genetic algorithms to solve multi-objective problems. These multi-objective genetic algorithms (MOGAs) are well suited to implementation in a Service Oriented Architecture. Often the evaluation process of the MOGA is compute-intensive due to the use of a complex computer model to represent the real-world system. The emerging paradigm of Grid Computing offers a potential solution to the compute-intensive nature of this objective function evaluation, by allowing access to large amounts of compute resources in a distributed manner. This paper presents a grid-enabled framework for multi-objective optimisation using genetic algorithms (MOGA-G) to aid decision making in engineering design

Computational steering of a multi-objective genetic algorithm using a PDA

The execution process of a genetic algorithm typically involves some trial-and-error. This is due to the difficulty in setting the initial parameters of the algorithm – especially when little is known about the problem domain. The problem is magnified when applied to multi-objective optimisation, as care is needed to ensure that the final population of candidate solutions is representative of the trade-off surface. We propose a computational steering system that allows the engineer to interact with the optimisation routine during execution. This interaction can be as simple as monitoring the values of some parameters during the execution process, or could involve altering those parameters to influence the quality of the solutions produce by the optimisation process

Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance

This paper proposes a novel multi-objective optimisation approach to solving both the problem of finding good structural and parametric choices in an ANN and the problem of training a classifier with a heavily skewed data set. The state-of-the-art CMA-PAES-HAGA multi-objective evolutionary algorithm [41] is used to simultaneously optimise the structure, weights, and biases of a population of ANNs with respect to not only the overall classification accuracy, but the classification accuracies of each individual target class. The effectiveness of this approach is then demonstrated on a real-world multi-class problem in medical diagnosis (classification of fetal cardiotocograms) where more than 75% of the data belongs to the majority class and the rest to two other minority classes. The optimised ANN is shown to significantly outperform a standard feed-forward ANN with respect to minority class recognition at the cost of slightly worse performance in terms of overall classification accuracy

A Multi objective Approach to Evolving Artificial Neural Networks for Coronary Heart Disease Classification

The optimisation of the accuracy of classifiers in pattern recognition is a complex problem that is often poorly understood. Whilst numerous techniques exist for the optimisa- tion of weights in artificial neural networks (e.g. the Widrow-Hoff least mean squares algorithm and back propagation techniques), there do not exist any hard and fast rules for choosing the structure of an artificial neural network - in particular for choosing both the number of the hidden layers used in the network and the size (in terms of number of neurons) of those hidden layers. However, this internal structure is one of the key factors in determining the accuracy of the classification. This paper proposes taking a multi-objective approach to the evolutionary design of artificial neural networks using a powerful optimiser based around the state-of-the-art MOEA/D- DRA algorithm and a novel method of incorporating decision maker preferences. In contrast to previous approaches, the novel approach outlined in this paper allows the intuitive consideration of trade-offs between classification objectives that are frequently present in complex classification problems but are often ignored. The effectiveness of the proposed multi-objective approach to evolving artificial neural networks is then shown on a real-world medical classification problem frequently used to benchmark classification method

CMA-PAES: Pareto archived evolution strategy using covariance matrix adaptation for multi-objective optimisation

The quality of Evolutionary Multi-Objective Optimisation (EMO) approximation sets can be measured by their proximity, diversity and pertinence. In this paper we introduce a modular and extensible Multi-Objective Evolutionary Algorithm (MOEA) capable of converging to the Pareto-optimal front in a minimal number of function evaluations and producing a diverse approximation set. This algorithm, called the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES), is a form of (μ + λ) Evolution Strategy which uses an online archive of previously found Pareto-optimal solutions (maintained by a bounded Pareto-archiving scheme) as well as a population of solutions which are subjected to variation using Covariance Matrix Adaptation. The performance of CMA-PAES is compared to NSGA-II (currently considered the benchmark MOEA in the literature) on the ZDT test suite of bi-objective optimisation problems and the significance of the results are analysed using randomisation testing. © 2012 IEEE

Evaluation of Mental Workload and Familiarity in Human Computer Interaction with Integrated Development Environments using Single-Channel EEG

With modern developments in sensing technology it has become possible to detect and classify brain activity into distinct states such as attention and relaxation using commercially avail- able EEG devices. These devices provide a low-cost and minimally intrusive method to observe a subject’s cognitive load whilst interacting with a computer system, thus providing a basis for deter- mining the overall effectiveness of the design of a computer interface. In this paper, a single-channel dry sensor EEG headset is used to record the mental effort and familiarity data of participants whilst they repeat a task eight times in either the Visual Studio or Eclipse Integrated Development Environments (IDEs). This data is used in conjunction with observed behaviour and perceived difficulties reported by the participants to suggest that human computer interaction with IDEs can be evaluated using mental effort and familiarity data retrieved by an affordable EEG headse

Socio-demographic and fertility related characteristics and motivations of oocyte donors in eleven European countries

Do the socio-demographic and fertility-related characteristics and motivations of oocyte donors differ in European countries? The socio-demographic and fertility-related characteristics and motivations of oocyte donors differ considerably across countries. There have been no other international studies comparing the characteristics of oocyte donors. Regarding their motivations, most studies indicate mixed motives. The proposed study was a transversal epidemiological study. Data were collected from 63 voluntarily participating assisted reproduction technology centres practising oocyte donation in 11 European countries (Belgium, Czech Republic, Finland, France, Greece, Poland, Portugal, Russia, Spain, UK and Ukraine). The survey was conducted between September 2011 and June 2012 and ran for 16 calendar months depending on the number of cycles of oocyte donation performed at the centre. The sample size was computed in order to allow an estimate of the percentage of a relatively rare characteristic (2) with a precision (95 confidence interval) of 1. The calculation gave 1118 donors. In total, 1423 forms were obtained from oocyte donors. All consecutive donors in these centres filled out an anonymous questionnaire when they started their hormonal stimulation, asking for their socio-demographic and fertility-related characteristics, their motivations and compensation. Population characteristics were described and compared by country of donation. Motives for donation and mean amount of money were compared between countries and according to the donors characteristics. The socio-demographic and fertility-related characteristics and motivations of oocyte donors varied enormously across European countries. The number of received forms corresponded with a participation rate of 61.9 of the cycles performed by the participating centres. Mean age was 27.4 years. About 49 of donors were fully employed, 16 unemployed and 15 student. The motivation in the total group of donors was 47.8 pure altruism, 33.9 altruism and financial, 10.8 pure financial, 5.9 altruism and own treatment and finally 2 own treatment only. About 15 of the donors were egg sharers (patient donors), mainly from the UK and Poland. Women were donating for the first time in 55.4 of cases, for the second time in 20.3 and for the third time in 12.8. The motivation to donate was significantly related to being of foreign origin (P 0.01), age (P 0.001), living in couple or not (P 0.01), level of education (P 0.001) and number of donations (P 0.001). The amount of compensation differed considerably between centres and/or countries. The general donor profile in this study was a well-educated, 27-year-old woman living with her partner and child who mainly donated to help others. The selection of clinics in some countries and the limited participation rate may have led to a bias in donor characteristics. A possible effect of social desirability in the answers by the donors should be taken into account. The diversity of the donor population reflects the differences in European legislation (for example, on anonymity and payment) and economic circumstances. The differences in systems of reimbursement/payment demonstrate the need to have a thorough discussion on the specific meaning of these terms. The study was funded by the European Society for Human Reproduction and Embryology. The authors declare no conflicting interests

ESHRE's good practice guide for cross-border reproductive care for centers and practitioners

This paper outlines ESHRE's guidance for centers and physicians providing fertility treatment to foreign patients. This guide aims to ensure high-quality and safe assisted reproduction treatment, taking into account the patients, their future child and the interests of third-party collaborators such as gametes donors and surrogates. This is achieved by including considerations of equity, safety, efficiency, effectiveness (including evidence-based care), timeliness and patient centeredness

Debating AI in archaeology: applications, implications, and ethical considerations

“Artificial Intelligence” (AI) is not a recent development. However, with increasing computational capabilities, AI has developed into Natural Language Processing and Machine Learning, technologies particularly good at detecting correlations and patterns, and categorising, predicting, or extracting information. Within archaeology, AI can process “big data” accumulated over decades of research and deposited in archives. By combining these capabilities, AI offers new insights and exciting opportunities to create knowledge from archaeological archives for contemporary and future research. However, ethical implications and human costs are not yet fully understood. Therefore, we question whether AI in archaeology is a blessing or a curse?Digital Archaeolog