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

Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation

There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing and parameter and model structural error. This paper presents a novel Markov chain Monte Carlo (MCMC) sampler, entitled differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a five-parameter rainfall-runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average watershed precipitation, information that is of utmost importance for testing hydrologic theory, diagnosing structural errors in models, and appropriately benchmarking rainfall measurement devices

Reduction in the efficiency of light use due to disease

Foram estudados os efeitos de uma micose causada por Ascochyta fabae na intercepção de luz solar pelo copado e no crescimento de Vicia faba, durante o seu cultivo no campo (Universidade de Nottingham, Inglaterra). Infecções precoces reduziram a “duração da área foliar” e a “eficiência de utilização de luz”, originando um decréscimo na produção de matéria seca da culturainfo:eu-repo/semantics/publishedVersio

Groundnut seedling emergence in relation to thermal-time and soil water

A medição do efeito de várias combinações de temperatura e água do solo sobre a emergência do amendoim permite verificar que as curvas de emergência relativamente à acumulação de temperatura são bem descritas por funções logísticas que se interpretam como curvas de probabilidade acumulada. Tais curvas permitem determinar facilmente o tempo-térmico para 50% da emergência final, a dispersão da emergência e a duração-térmica para ocorrência duma emergência de 80% da final. Verifica-se que estes parâmetros são praticamente invariantes (relativamente ao tempo, à temperatura e à água) para teores de água no solo superiores a 45% da capacidade de campo e temperaturas inferiores à óptima para a emergência, pelo que a sua medição pontual é representativa para toda esta gama de condições ambientaisinfo:eu-repo/semantics/publishedVersio

Impact of pressure drop oscillations on surface temperature and critical heat flux during flow boiling in a microchannel

Flow boiling in microchannel heat sinks is capable of providing the high-heat-flux dissipation required for thermal management of next-generation wide bandgap power electronics at low pumping power and uniform surface temperatures. One of the primary issues preventing implementation of these technologies is the presence of flow boiling instabilities, which may reduce the heat transfer performance. However, the effect of individual instabilities, such as the parallel channel instability or pressure drop oscillations, on the overall heat transfer coefficient and critical heat flux in microchannel heat sinks has not been fully quantified. The primary cause of these dynamic flow boiling instabilities is the interaction between the inertia of a two-phase mixture in a heated channel and sources of compressibility located upstream of the inlet. In order to isolate the effect of pressure drop oscillations on flow boiling heat transfer performance, experiments are performed in a single square microchannel cut into a copper heat sink, with a controlled level of upstream compressibility. The impact of pressure drop oscillations on the heat transfer coefficient and critical heat flux is characterized through analysis of both time-averaged steady-state data as well as high-frequency pressure signals synchronized with high-speed visualization. The dielectric working fluid HFE-7100 is used in all experiments with a saturation temperature of 60°C at the channel outlet pressure. The occurrence and effect of pressure drop oscillations in 20 mm long microchannels of three different channel widths (0.5, 0.75, and 1 mm) are related to mass flux, the degree of two-phase flow confinement, and the severity of pressure drop oscillations

Trustworthy placements: Improving quality and resilience in collaborative attack detection

Abstract In distributed and collaborative attack detection systems decisions are made on the basis of the events reported by many sensors, e.g., Intrusion Detection Systems placed across various network locations. In some cases such events originate at locations over which we have little control, for example because they belong to an organisation that shares information with us. Blindly accepting such reports as real encompasses several risks, as sensors might be dishonest, unreliable or simply having been compromised. In these situations trust plays an important role in deciding whether alerts should be believed or not. In this work we present an approach to maximise the quality of the information gathered in such systems and the resilience against dishonest behaviours. We introduce the notion of trust diversity amongst sensors and argue that detection configurations with such a property perform much better in many respects. Using reputation as a proxy for trust, we introduce an adaptive scheme to dynamically reconfigure the network of detection sensors. Experiments confirm an overall increase both in detection quality and resilience against compromise and misbehaviour

Trustworthy placements: Improving quality and resilience in collaborative attack detection

Abstract In distributed and collaborative attack detection systems decisions are made on the basis of the events reported by many sensors, e.g., Intrusion Detection Systems placed across various network locations. In some cases such events originate at locations over which we have little control, for example because they belong to an organisation that shares information with us. Blindly accepting such reports as real encompasses several risks, as sensors might be dishonest, unreliable or simply having been compromised. In these situations trust plays an important role in deciding whether alerts should be believed or not. In this work we present an approach to maximise the quality of the information gathered in such systems and the resilience against dishonest behaviours. We introduce the notion of trust diversity amongst sensors and argue that detection configurations with such a property perform much better in many respects. Using reputation as a proxy for trust, we introduce an adaptive scheme to dynamically reconfigure the network of detection sensors. Experiments confirm an overall increase both in detection quality and resilience against compromise and misbehaviour

Optimising trotter-suzuki decompositions for quantum simulation using evolutionary strategies

One of the most promising applications of near-term quantum computing is the simulation of quantum systems, a classically intractable task. Quantum simulation requires computationally expensive matrix exponentiation; Trotter-Suzuki decomposition of this exponentiation enables efficient simulation to a desired accuracy on a quantum computer. We apply the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm to optimise the Trotter-Suzuki decompositions of a canonical quantum system, the Heisenberg Chain; we reduce simulation error by around 60%. We introduce this problem to the computational search community, show that an evolutionary optimisation approach is robust across runs and problem instances, and find that optimisation results generalise to the simulation of larger systems