Elite Accumulative Sampling Strategies for Noisy Multi-Objective Optimisation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-15892-1_128th International Conference on Evolutionary Multi-Criterion Optimization 2015, Guimarães, Portugal, 29 March - 1 April 1 2015The codebase for this paper is available at https://github.com/fieldsend/EMO_2015_eliteWhen designing evolutionary algorithms one of the key concerns is the balance between expending function evaluations on exploration versus exploitation. When the optimisation problem experiences observational noise, there is also a trade-off with respect to accuracy refinement – as improving the estimate of a design’s performance typically is at the cost of additional function reevaluations. Empirically the most effective resampling approach developed so far is accumulative resampling of the elite set. In this approach elite members are regularly reevaluated, meaning they progressively accumulate reevaluations over time. This results in their approximated objective values having greater fidelity, meaning non-dominated solutions are more likely to be correctly identified. Here we examine four different approaches to accumulative resampling of elite members, embedded within a differential evolution algorithm. Comparing results on 40 variants of the unconstrained IEEE CEC’09 multi-objective test problems, we find that at low noise levels a low fixed resample rate is usually sufficient, however for larger noise magnitudes progressively raising the number of minimum resamples of elite members based on detecting estimated front oscillation tends to improve performance

On the Exploitation of Search History and Accumulative Sampling in Robust Optimisation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.Efficient robust optimisation methods exploit the search history when evaluating a new solution by using information from previously visited solutions that fall in the new solution’s uncertainty neighbourhood. We propose a full exploitation of the search history by updating the robust fitness approximations across the entire search history rather than a fixed population. Our proposed method shows promising results on a range of test problems compared with other approaches from the literature.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1]

Genetic Algorithms in Stochastic Optimization and Applications in Power Electronics

Genetic Algorithms (GAs) are widely used in multiple fields, ranging from mathematics, physics, to engineering fields, computational science, bioinformatics, manufacturing, economics, etc. The stochastic optimization problems are important in power electronics and control systems, and most designs require choosing optimum parameters to ensure maximum control effect or minimum noise impact; however, they are difficult to solve using the exhaustive searching method, especially when the search domain conveys a large area or is infinite. Instead, GAs can be applied to solve those problems. And efficient computing budget allocation technique for allocating the samples in GAs is necessary because the real-life problems with noise are often difficult to evaluate and require significant computation effort. A single objective GA is proposed in which computing budget allocation techniques are integrated directly into the selection operator rather than being used during fitness evaluation. This allows fitness evaluations to be allocated towards specific individuals for whom the algorithm requires more information, and this selection-integrated method is shown to be more accurate for the same computing budget than the existing evaluation-integrated methods on several test problems. A combination of studies is performed on a multi-objective GA that compares integration of different computing budget allocation methods into either the evaluation or the environmental selection steps. These comparisons are performed on stochastic problems derived from benchmark multi-objective optimization problems and consider varying levels of noise. The algorithms are compared regarding both proximity to and coverage of the true Pareto-optimal front, and sufficient studies are performed to allow statistically significant conclusions to be drawn. Finally, the multi-objective GA with selection integrated sampling technique is applied to solve a multi-objective stochastic optimization problem in a grid connected photovoltaic inverter system with noise injected from both the solar power input and the utility grid

Noisy combinatorial optimisation with evolutionary algorithms

The determination of the efficient evolutionary optimisation approaches in solving noisy combinatorial problems is the main focus in this research. Initially, we present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are four sets of experiments. The first looks at several toy problems, such as OneMax and other linear problems. We find that Univariate Marginal Distribution Algorithm (UMDA) and the Paired-Crossover Evolutionary Algorithm (PCEA) are the only ones able to cope robustly with noise, within a reasonable fixed time budget. In the second stage, UMDA and PCEA are then tested on more complex noisy problems: SubsetSum, Knapsack and SetCover. Both perform well under increasing levels of noise, with UMDA being the better of the two. In the third stage, we consider two noisy multi-objective problems (CountingOnesCountingZeros and a multi-objective formulation of SetCover). We compare several adaptations of UMDA for multi-objective problems with the Simple Evolutionary Multi-objective Optimiser (SEMO) and NSGA-II. In the last stage of empirical analysis, a realistic problem of the path planning for the ground surveillance with Unmanned Aerial Vehicles is considered. We conclude that UMDA, and its variants, can be highly effective on a variety of noisy combinatorial optimisation, outperforming many other evolutionary algorithms. Next, we study the use of voting mechanisms in populations, and introduce a new Voting algorithm which can solve OneMax and Jump in O(n log n), even for gaps as large as O(n). More significantly, the algorithm solves OneMax with added posterior noise in O(n log n), when the variance of the noise distribution is sigma$^2$ = O(n) and in O(sigma$^2$ log n) when the noise variance is greater than this. We assume only that the noise distribution has finite mean and variance and (for the larger noise case) that it is unimodal. Building upon this promising performance, we consider other noise models prevalent in optimisation and learning and show that the Voting algorithm has efficient performance in solving OneMax in presence of these noise variants. We also examine the performance on arbitrary linear and monotonic functions. The Voting algorithm fails on LeadingOnes but we give a variant which can solve the problem in O(n log n). We empirically study the use of voting in population based algorithms (UMDA, PCEA and cGA) and show that this can be effective for large population sizes

Type-2 fuzzy logic system applications for power systems

PhD ThesisIn the move towards ubiquitous information & communications technology, an opportunity for further optimisation of the power system as a whole has arisen. Nonetheless, the fast growth of intermittent generation concurrently with markets deregulation is driving a need for timely algorithms that can derive value from these new data sources. Type-2 fuzzy logic systems can offer approximate solutions to these computationally hard tasks by expressing non-linear relationships in a more flexible fashion. This thesis explores how type-2 fuzzy logic systems can provide solutions to two of these challenging power system problems; short-term load forecasting and voltage control in distribution networks. On one hand, time-series forecasting is a key input for economic secure power systems as there are many tasks that require a precise determination of the future short-term load (e.g. unit commitment or security assessment among others), but also when dealing with electricity as commodity. As a consequence, short-term load forecasting becomes essential for energy stakeholders and any inaccuracy can be directly translated into their financial performance. All these is reflected in current power systems literature trends where a significant number of papers cover the subject. Extending the existing literature, this work focuses in how these should be implemented from beginning to end to bring to light their predictive performance. Following this research direction, this thesis introduces a novel framework to automatically design type-2 fuzzy logic systems. On the other hand, the low-carbon economy is pushing the grid status even closer to its operational limits. Distribution networks are becoming active systems with power flows and voltages defined not only by load, but also by generation. As consequence, even if it is not yet absolutely clear how power systems will evolve in the long-term, all plausible future scenarios claim for real-time algorithms that can provide near optimal solutions to this challenging mixed-integer non-linear problem. Aligned with research and industry efforts, this thesis introduces a scalable implementation to tackle this task in divide-and-conquer fashio

Genetic Improvement of Software for Energy E ciency in Noisy and Fragmented Eco-Systems

Software has made its way to every aspect of our daily life. Users of smart devices expect almost continuous availability and uninterrupted service. However, such devices operate on restricted energy resources. As energy eficiency of software is relatively a new concern for software practitioners, there is a lack of knowledge and tools to support the development of energy eficient software. Optimising the energy consumption of software requires measuring or estimating its energy use and then optimising it. Generalised models of energy behaviour suffer from heterogeneous and fragmented eco-systems (i.e. diverse hardware and operating systems). The nature of such optimisation environments favours in-vivo optimisation which provides the ground-truth for energy behaviour of an application on a given platform. One key challenge in in-vivo energy optimisation is noisy energy readings. This is because complete isolation of the effects of software optimisation is simply infeasible, owing to random and systematic noise from the platform. In this dissertation we explore in-vivo optimisation using Genetic Improvement of Software (GI) for energy eficiency in noisy and fragmented eco-systems. First, we document expected and unexpected technical challenges and their solutions when conducting energy optimisation experiments. This can be used as guidelines for software practitioners when conducting energy related experiments. Second, we demonstrate the technical feasibility of in-vivo energy optimisation using GI on smart devices. We implement a new approach for mitigating noisy readings based on simple code rewrite. Third, we propose a new conceptual framework to determine the minimum number of samples required to show significant differences between software variants competing in tournaments. We demonstrate that the number of samples can vary drastically between different platforms as well as from one point of time to another within a single platform. It is crucial to take into consideration these observations when optimising in the wild or across several devices in a control environment. Finally, we implement a new validation approach for energy optimisation experiments. Through experiments, we demonstrate that the current validation approaches can mislead software practitioners to draw wrong conclusions. Our approach outperforms the current validation techniques in terms of specificity and sensitivity in distinguishing differences between validation solutions.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

SODECL: An Open-Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel

This is the final version. Available on open access from ACM via the DOI in this recordStochastic differential equations (SDEs) are widely used to model systems affected by random processes. In general, the analysis of an SDE model requires numerical solutions to be generated many times over multiple parameter combinations. However, this process often requires considerable computational resources to be practicable. Due to the embarrassingly parallel nature of the task, devices such as multi-core processors and graphics processing units (GPUs) can be employed for acceleration. Here, we present SODECL (https://github.com/avramidis/sodecl), a software library that utilizes such devices to calculate multiple orbits of an SDE model. To evaluate the acceleration provided by SODECL, we compared the time required to calculate multiple orbits of an exemplar stochastic model when one CPU core is used, to the time required when using all CPU cores or a GPU. In addition, to assess scalability, we investigated how model size affected execution time on different parallel compute devices. Our results show that when using all 32 CPU cores of a high-end high-performance computing node, the task is accelerated by a factor of up to ≈ 6.7, compared to when using a single CPU core. Executing the task on a high-end GPU yielded accelerations of up to ≈ 4.5, compared to a single CPU core.Engineering and Physical Sciences Research Council (EPSRC

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field