7 research outputs found

    Examination scheduling using the ant system.

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    This work is concerned with heuristic approaches to examination timetabling. It is demonstrated that a relatively new evolutionary method, the Ant System, can be the basis of a successful two-phase solution method. The first phase exploits ant feedback in order both to produce large volumes of feasible timetables and to optimise secondary objectives. The second phase acts as a repair facility where solution quality is improved further while maintaining feasibility. This is accomplished without increasing computational effort to unrealistic levels. The work builds on an existing implementation for the graph colouring problem, the natural model for examination scheduling. It is demonstrated that by adjusting the graph model to allow the accommodation of several side constraints as well incorporating enhancement techniques within the algorithm itself, the Ant System algorithm becomes very effective at producing feasible timetables. The enhancements include a diversification function, new reward functions and trail replenishment tactics. It is observed that the achievement of second-order objectives can be enhanced through a variety of means. A modified elitist strategy (ERF) significantly improves the performance of the Ant System due to the extra emphasis on second-order feedback. It is also shown that through the incorporation of the ERF, trail limits and, in particular, 19th century evolutionary theory the area of the solution space explored by the ants during the infancy of the search can be reduced. In addition, a good level of exploration is maintained as the search matures. This balance between exploration and exploitation is the main determinant of solution quality. The use of a repair facility, as is common practice with evolutionary algorithms, encourages fitter solutions. The interaction between Lamarckian evolution and searching in an extended neighbourhood through the graph theoretic concept of Kempe chains leads to better overall solutions

    Applications and enhancements of aircraft design optimization techniques

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    The aircraft industry has been at the forefront in developing design optimization strategies ever since the advent of high performance computing. Thanks to the large computational resources now available, many new as well as more mature optimization methods have become well established. However, the same cannot be said for other stages along the optimization process - chiefly, and this is where the present thesis seeks to make its first main contribution, at the geometry parameterization stage.The first major part of the thesis is dedicated to the goal of reducing the size of the search space by reducing the dimensionality of existing parameterization schemes, thus improving the effectiveness of search strategies based upon them. Specifically, a refinement to the Kulfan parameterization method is presented, based on using Genetic Programming and a local search within a Baldwinian learning strategy to evolve a set of analytical expressions to replace the standard 'class function' at the basis of the Kulfan method. The method is shown to significantly reduce the number of parameters and improves optimization performance - this is demonstrated using a simple aerodynamic design case study.The second part describes an industrial level case study, combining sophisticated, high fidelity, as well as fast, low fidelity numerical analysis with a complex physical experiment. The objective is the analysis of a topical design question relating to reducing the environmental impact of aviation: what is the optimum layout of an over-the-wing turbofan engine installation designed to enable the airframe to shield near-airport communities on the ground from fan noise. An experiment in an anechoic chamber reveals that a simple half-barrier noise model can be used as a first order approximation to the change of inlet broadband noise shielding by the airframe with engine position, which can be used within design activities. Moreover, the experimental results are condensed into an acoustic shielding performance metric to be used in a Multidisciplinary Design Optimization study, together with drag and engine performance values acquired through CFD. By using surrogate models of these three performance metrics we are able to find a set of non-dominated engine positions comprising a Pareto Front of these objectives. This may give designers of future aircraft an insight into an appropriate engine position above a wing, as well as a template for blending multiple levels of computational analysis with physical experiments into a multidisciplinary design optimization framework

    Music in Evolution and Evolution in Music

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    Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa. Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon. A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Angle modulated population based algorithms to solve binary problems

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    Recently, continuous-valued optimization problems have received a great amount of focus, resulting in optimization algorithms which are very efficient within the continuous-valued space. Many optimization problems are, however, defined within the binary-valued problem space. These continuous-valued optimization algorithms can not operate directly on a binary-valued problem representation, without algorithm adaptations because the mathematics used within these algorithms generally fails within a binary problem space. Unfortunately, such adaptations may alter the behavior of the algorithm, potentially degrading the performance of the original continuous-valued optimization algorithm. Additionally, binary representations present complications with respect to increasing problem dimensionality, interdependencies between dimensions, and a loss of precision. This research investigates the possibility of applying continuous-valued optimization algorithms to solve binary-valued problems, without requiring algorithm adaptation. This is achieved through the application of a mapping technique, known as angle modulation. Angle modulation effectively addresses most of the problems associated with the use of a binary representation by abstracting a binary problem into a four-dimensional continuous-valued space, from which a binary solution is then obtained. The abstraction is obtained as a bit-generating function produced by a continuous-valued algorithm. A binary solution is then obtained by sampling the bit-generating function. This thesis proposes a number of population-based angle-modulated continuous-valued algorithms to solve binary-valued problems. These algorithms are then compared to binary algorithm counterparts, using a suite of benchmark functions. Empirical analysis will show that the angle-modulated continuous-valued algorithms are viable alternatives to binary optimization algorithms. Copyright 2012, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Pamparà, G 2012, Angle modulated population based algorithms to solve binary problems, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd C12/4/188/gmDissertation (MSc)--University of Pretoria, 2012.Computer Scienceunrestricte

    A complex systems approach to education in Switzerland

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    The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance
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