Evolutionary improvement of programs

Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues

General practitioner empathy, patient enablement, and patient-reported outcomes in primary care in an area of high socio-economic deprivation in Scotland - a pilot prospective study using structural equation modelling

<b>Objective</b> The aim of this pilot prospective study was to investigate the relationships between general practitioners (GPs) empathy, patient enablement, and patient-assessed outcomes in primary care consultations in an area of high socio-economic deprivation in Scotland.<p></p> <b>Methods</b> This prospective study was carried out in a five-doctor practice in an area of high socio-economic deprivation in Scotland. Patients’ views on the consultation were gathered using the Consultation and Relational Empathy (CARE) Measure and the Patient Enablement Instrument (PEI). Changes in main complaint and well-being 1 month after the contact consultation were gathered from patients by postal questionnaire. The effect of GP empathy on patient enablement and prospective change in outcome was investigated using structural equation modelling.<p></p> <b>Results</b> 323 patients completed the initial questionnaire at the contact consultation and of these 136 (42%) completed and returned the follow-up questionnaire at 1 month. Confirmatory factor analysis confirmed the construct validity of the CARE Measure, though omission of two of the six PEI items was required in order to reach an acceptable global data fit. The structural equation model revealed a direct positive relationship between GP empathy and patient enablement at contact consultation and a prospective relationship between patient enablement and changes in main complaint and well-being at 1 month.<p></p> <b>Conclusion</b> In a high deprivation setting, GP empathy is associated with patient enablement at consultation, and enablement predicts patient-rated changes 1 month later. Further larger studies are desirable to confirm or refute these findings.<p></p> <b>Practice implications</b> Ways of increasing GP empathy and patient enablement need to be established in order to maximise patient outcomes. Consultation length and relational continuity of care are known factors; the benefit of training and support for GPs needs to be further investigate

Estimating the feasibility of transition paths in extended finite state machines

There has been significant interest in automating testing on the basis of an extended finite state machine (EFSM) model of the required behaviour of the implementation under test (IUT). Many test criteria require that certain parts of the EFSM are executed. For example, we may want to execute every transition of the EFSM. In order to find a test suite (set of input sequences) that achieves this we might first derive a set of paths through the EFSM that satisfy the criterion using, for example, algorithms from graph theory. We then attempt to produce input sequences that trigger these paths. Unfortunately, however, the EFSM might have infeasible paths and the problem of determining whether a path is feasible is generally undecidable. This paper describes an approach in which a fitness function is used to estimate how easy it is to find an input sequence to trigger a given path through an EFSM. Such a fitness function could be used in a search-based approach in which we search for a path with good fitness that achieves a test objective, such as executing a particular transition, and then search for an input sequence that triggers the path. If this second search fails then we search for another path with good fitness and repeat the process. We give a computationally inexpensive approach (fitness function) that estimates the feasibility of a path. In order to evaluate this fitness function we compared the fitness of a path with the ease with which an input sequence can be produced using search to trigger the path and we used random sampling in order to estimate this. The empirical evidence suggests that a reasonably good correlation (0.72 and 0.62) exists between the fitness of a path, produced using the proposed fitness function, and an estimate of the ease with which we can randomly generate an input sequence to trigger the path

Use of quercetin in animal feed : effects on the P-gp expression and pharmacokinetics of orally administrated enrofloxacin in chicken

Modulation of P-glycoprotein (P-gp, encoded by Mdr1) by xenobiotics plays central role in pharmacokinetics of various drugs. Quercetin has a potential to modulate P-gp in rodents, however, its effects on P-gp modulation in chicken are still unclear. Herein, study reports role of quercetin in modulation of P-gp expression and subsequent effects on the pharmacokinetics of enrofloxacin in broilers. Results show that P-gp expression was increased in a dose-dependent manner following exposure to quercetin in Caco-2 cells and tissues of chicken. Absorption rate constant and apparent permeability coefficient of rhodamine 123 were decreased, reflecting efflux function of P-gp in chicken intestine increased by quercetin. Quercetin altered pharmacokinetic of enrofloxacin by decreasing area under curve, peak concentration, and time to reach peak concentration and by increasing clearance rate. Molecular docking shows quercetin can form favorable interactions with binding pocket of chicken xenobiotic receptor (CXR). Results provide convincing evidence that quercetin induced P-gp expression in tissues by possible interaction with CXR, and consequently reducing bioavailability of orally administered enrofloxacin through restricting its intestinal absorption and liver/kidney clearance in broilers. The results can be further extended to guide reasonable use of quercetin to avoid drug-feed interaction occurred with co-administered enrofloxacin or other similar antimicrobials.Peer reviewedFinal Published versio

Learning and Designing Stochastic Processes from Logical Constraints

Stochastic processes offer a flexible mathematical formalism to model and reason about systems. Most analysis tools, however, start from the premises that models are fully specified, so that any parameters controlling the system's dynamics must be known exactly. As this is seldom the case, many methods have been devised over the last decade to infer (learn) such parameters from observations of the state of the system. In this paper, we depart from this approach by assuming that our observations are {\it qualitative} properties encoded as satisfaction of linear temporal logic formulae, as opposed to quantitative observations of the state of the system. An important feature of this approach is that it unifies naturally the system identification and the system design problems, where the properties, instead of observations, represent requirements to be satisfied. We develop a principled statistical estimation procedure based on maximising the likelihood of the system's parameters, using recent ideas from statistical machine learning. We demonstrate the efficacy and broad applicability of our method on a range of simple but non-trivial examples, including rumour spreading in social networks and hybrid models of gene regulation

Integrated system to perform surrogate based aerodynamic optimisation for high-lift airfoil

This work deals with the aerodynamics optimisation of a generic two-dimensional three element high-lift configuration. Although the high-lift system is applied only during take-off and landing in the low speed phase of the flight the cost efficiency of the airplane is strongly influenced by it [1]. The ultimate goal of an aircraft high lift system design team is to define the simplest configuration which, for prescribed constraints, will meet the take-off, climb, and landing requirements usually expressed in terms of maximum L/D and/or maximum CL. The ability of the calculation method to accurately predict changes in objective function value when gaps, overlaps and element deflections are varied is therefore critical. Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimisation. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in place of the actual simulation models. This work outlines the development of integrated systems to perform aerodynamics multi-objective optimisation for a three-element airfoil test case in high lift configuration, making use of surrogate models available in MACROS Generic Tools, which has been integrated in our design tool. Different metamodeling techniques have been compared based on multiple performance criteria. With MACROS is possible performing either optimisation of the model built with predefined training sample (GSO) or Iterative Surrogate-Based Optimization (SBO). In this first case the model is build independent from the optimisation and then use it as a black box in the optimisation process. In the second case is needed to provide the possibility to call CFD code from the optimisation process, and there is no need to build any model, it is being built internally during the optimisation process. Both approaches have been applied. A detailed analysis of the integrated design system, the methods as well as th