http://www.bibsonomy.org/burst/concept/tag/digitalBibSonomy BuRST Feed for /concept/tag/digital2008-07-21T00:35:28+02:00http://www.bibsonomy.org/bibtex/20507fdd22e0b1b1cce785193307704f6/schulteschulte2008-06-22T18:41:25+02:00cscw space library binding automatic digital knowledge distributed <span style="color:#555555;">Thomas <a href="http://www.bibsonomy.org/author/Bopp">Bopp</a> and Jonas <a href="http://www.bibsonomy.org/author/Schulte">Schulte</a> and Thorsten <a href="http://www.bibsonomy.org/author/Hampel">Hampel</a> </span><em>ICEIS 2007 - 9th International Conference on Enterprise Information Systems, </em><em>DISI, </em><em>page323--329. </em><em>Funchal, Madeira - Portugal, </em><em>June2007. </em>Sun Jun 22 18:41:25 CEST 2008Funchal, Madeira - PortugalICEIS 2007 - 9th International Conference on Enterprise Information SystemsJune323--329Enabling CSCW Systems to Automatically Bind External Knowledge BasesDISI2007cscw space library binding automatic digital knowledge distributed The usage of CSCW systems for teaching, training, and research collaboration increases constantly, as it offers a time- and place-independent, as well as a cost-effective platform. The user's search should not be restricted to local material; in fact, users benefit from different search environments, as, for example digital libraries to find appropriate working material. Searching and further processing of documents imply a media breach since the search cannot be invoked in current CSCW systems directly. This paper presents the first prototype of a CSCW system which enables users to search in external sources without media breach. To provide arbitrary search environments no restrictions to data formats or search functionalities are allowed. Hence we have enhanced search environments with self description capabilities in order to realize an automatic binding of search environments in CSCW systems. By search environments we address any system offering searchable knowledge bases, such as digital libraries or the CSCW system itself. Furthermore our concept supports local search and searching in different external sources at the same time.http://www.bibsonomy.org/bibtex/22b40066633867817f3e17dd66e29c0c3/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00evolvable digital reasoning, based computing, programming, hardware, case genetic principle fitness extraction algorithms, evolutionary design, landscapes, circuit circuits, <span style="color:#555555;">Julian F. <a href="http://www.bibsonomy.org/author/Miller">Miller</a> and Dominic <a href="http://www.bibsonomy.org/author/Job">Job</a> and Vesselin K. <a href="http://www.bibsonomy.org/author/Vassilev">Vassilev</a> </span><em>Genetic Programming and Evolvable Machines</em><em>1(3):259--288</em><em>July2000. </em>Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesJuly3259--288Principles in the Evolutionary Design of Digital Circuits-Part {II}12000evolvable digital reasoning, based computing, programming, hardware, case genetic principle fitness extraction algorithms, evolutionary design, landscapes, circuit circuits, In a previous work it was argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design. These ideas are tested in the context of designing digital circuits, particularly arithmetic circuits. This process of discovery is seen as a principle extraction loop in which the evolved data is analysed both phenotypically and genotypically by processes of data mining and landscape analysis. The information extracted is then fed back into the evolutionary algorithm to enhance its search capabilities and hence increase the likelihood of identifying new principles which explain how to build systems which are too large to evolve.http://www.bibsonomy.org/bibtex/211a583a223fe755ff5ec75f3fb517308/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00programming, EVOLUTION, algorithms, EXPERIMENTAL alife, SELF-REPLICATING PROGRAMS ORGANISMS, genetic DIGITAL COMPUTER <span style="color:#555555;">Charles <a href="http://www.bibsonomy.org/author/Ofria">Ofria</a> and Claus O. <a href="http://www.bibsonomy.org/author/Wilke">Wilke</a> </span><em>Artificial Life</em><em>10(2):191--229</em><em>1 April2004. </em>Thu Jun 19 17:46:40 CEST 2008Artificial Life1 April2191--229Avida: {A} Software Platform for Research in Computational Evolutionary Biology102004programming, EVOLUTION, algorithms, EXPERIMENTAL alife, SELF-REPLICATING PROGRAMS ORGANISMS, genetic DIGITAL COMPUTER Avida is a software platform for experiments with self-replicating and evolving computer programs. It provides detailed control over experimental settings and protocols, a large array of measurement tools, and sophisticated methods to analyse and post-process experimental data. We explain the general principles on which Avida is built, as well as its main components and their interactions. We also explain how experiments are set up, carried out, and analyzed.http://www.bibsonomy.org/bibtex/26e2c03fad647d9c7e71585baadb27f9e/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00expression circuits multi genetic digital algorithms, programming, <span style="color:#555555;">Mihai <a href="http://www.bibsonomy.org/author/Oltean">Oltean</a> </span><em>7th Joint Conference on Information Sciences, </em><em>1, </em><em>page315--318. </em><em>North Carolina, </em><em>Association for Intelligent Machinery, </em><em>September2003. </em>Thu Jun 19 17:46:40 CEST 2008North Carolina7th Joint Conference on Information SciencesSeptember315--318Solving Even-Parity Problems using Multi Expression Programming12003expression circuits multi genetic digital algorithms, programming, Multi Expression Programming (MEP) is used for solving even-parity problems. Numerical experiments show that MEP outperforms Genetic Programming (GP) with more than one order of magnitude for the considered test cases.http://www.bibsonomy.org/bibtex/25756c465f3d74947215babb98adc39b2/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00genetic circuits algorithms, programming, digital expression multi <span style="color:#555555;">Mihai <a href="http://www.bibsonomy.org/author/Oltean">Oltean</a> and Crina <a href="http://www.bibsonomy.org/author/Grosan">Grosan</a> </span><em>Proceedings of the 2004 NASA/DoD Conference on Evolvable Hardware, </em><em>page87--97. </em><em>Seattle, </em><em>IEEE Press, </em><em>24-26 June2004. </em>Thu Jun 19 17:46:40 CEST 2008SeattleProceedings of the 2004 NASA/DoD Conference on Evolvable Hardware24-26 June87--97Evolving Digital Circuits using Multi Expression Programming2004genetic circuits algorithms, programming, digital expression multi Multi Expression Programming (MEP) is a Genetic Programming (GP) variant that uses linear chromosomes for solution encoding. A unique MEP feature is its ability of encoding multiple solutions of a problem in a single chromosome. These solutions are handled in the same time complexity as other techniques that encode a single solution in a chromosome. In this paper MEP is used for evolving digital circuits. MEP is compared to Cartesian Genetic Programming (CGP) a technique widely used for evolving digital circuits by using several well-known problems in the field of electronic circuit design. Numerical experiments show that MEP outperforms CGP for the considered test problems.http://www.bibsonomy.org/bibtex/25bb63016ff80ebd3f5a1d2aa8fc97e15/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Tierra, Open-ended organisms, programming, genetic evolution; digital complexity, algorithms, neutral networks <span style="color:#555555;">Russell K. <a href="http://www.bibsonomy.org/author/Standish">Standish</a> </span><em>International Journal of Computational Intelligence and Applications</em><em>3(2):167--175</em>(<em>2003</em>)Thu Jun 19 17:46:40 CEST 2008International Journal of Computational Intelligence and Applications2167--175Open-ended artificial evolution32003Tierra, Open-ended organisms, programming, genetic evolution; digital complexity, algorithms, neutral networks Of all the issues discussed at Alife VII: Looking Forward, Looking Backward, the issue of whether it was possible to create an artificial life system that exhibits open-ended evolution of novelty is by far the biggest. Of the 14 open problems settled on as a result of debate at the conference, some 6 are directly, or indirectly related to this issue. Most people equate open-ended evolution with complexity growth, although a priori these seem to be different things. In this paper I report on experiments to measure the complexity of Tierran organisms, and show the results for a size-neutral run of Tierra. In this run, no increase in organismal complexity was observed, although organism size did increase through the run. This result is discussed, offering some signposts on path to solving the issue of open ended evolution.http://www.bibsonomy.org/bibtex/2245342a46221b658ca61bc5a50b03c27/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00evolvable hardware, logic, classifier genetic FPGA systems, algorithms, digital <span style="color:#555555;">Jim <a href="http://www.bibsonomy.org/author/Torresen">Torresen</a> </span><em>Genetic Programming and Evolvable Machines</em><em>3(3):259--282</em><em>September2002. </em>Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesSeptember3259--282A Scalable Approach to Evolvable Hardware32002evolvable hardware, logic, classifier genetic FPGA systems, algorithms, digital Evolvable Hardware (EHW) has been proposed as a new method for designing systems for complex real-world applications. However, so far, only relatively simple systems have been shown to be evolvable. In this paper, it is proposed that concepts from biology should be applied to EHW techniques to make EHW more applicable to solving complex problems. One such concept has led to the increased complexity scheme presented, where a system is evolved by evolving smaller sub-systems. Experiments with two different tasks illustrate that inclusion of this scheme substantially reduces the number of generations required for evolution. Further, for the prosthesis control task, the best performance is obtained by the novel approach. The best circuit evolved performs better than the best trained neural network.http://www.bibsonomy.org/bibtex/24c41d66d6d01443cc2dde71aec44b735/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00adders, acquisition, design, genetic multipliers, modularity, computational effort, performance digital algorithms, module programming, comparators, cartesian <span style="color:#555555;">James Alfred <a href="http://www.bibsonomy.org/author/Walker">Walker</a> and Julian Francis <a href="http://www.bibsonomy.org/author/Miller">Miller</a> </span><em>GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, </em><em>2, </em><em>page1649--1656. </em><em>Washington DC, USA, </em><em>ACM Press, </em><em>25-29 June2005. </em>Thu Jun 19 17:46:40 CEST 2008Washington DC, USA{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation25-29 June1649--1656Investigating the performance of module acquisition in cartesian genetic programming22005adders, acquisition, design, genetic multipliers, modularity, computational effort, performance digital algorithms, module programming, comparators, cartesian http://www.bibsonomy.org/bibtex/24ef4b6004cc046b1d75259dacf702d87/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00programming, strategy, synthesis Cartesian algorithms, evolutionary acquisition, program synthesis, embedded circuits, ES, multi-chromosome, multi-chromosome evolution, functions, defined digital module automatically genetic <span style="color:#555555;">James Alfred <a href="http://www.bibsonomy.org/author/Walker">Walker</a> and Julian Francis <a href="http://www.bibsonomy.org/author/Miller">Miller</a> and Rachel <a href="http://www.bibsonomy.org/author/Cavill">Cavill</a> </span><em>GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, </em><em>1, </em><em>page903--910. </em><em>Seattle, Washington, USA, </em><em>ACM Press, </em><em>8-12 July2006. </em>Thu Jun 19 17:46:40 CEST 2008Seattle, Washington, USA{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation8-12 July903--910A multi-chromosome approach to standard and embedded cartesian genetic programming12006programming, strategy, synthesis Cartesian algorithms, evolutionary acquisition, program synthesis, embedded circuits, ES, multi-chromosome, multi-chromosome evolution, functions, defined digital module automatically genetic http://www.bibsonomy.org/bibtex/2c6cb32bb403dac1467cca453e21a91d0/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00circuits, parallel design, evolvable algorithms, programming, digital FPGA, hardware, Circuit genetic programming <span style="color:#555555;">Sin Man <a href="http://www.bibsonomy.org/author/Cheang">Cheang</a> and Kin Hong <a href="http://www.bibsonomy.org/author/Lee">Lee</a> and Kwong Sak <a href="http://www.bibsonomy.org/author/Leung">Leung</a> </span><em>IEEE Transactions on Evolutionary Computation</em><em>11(4):503--520</em><em>August2007. </em>Thu Jun 19 17:35:00 CEST 2008IEEE Transactions on Evolutionary ComputationAugust4503--520Applying Genetic Parallel Programming to Synthesize Combinational Logic Circuits112007circuits, parallel design, evolvable algorithms, programming, digital FPGA, hardware, Circuit genetic programming Experimental results show that parallel programs can be evolved more easily than sequential programs in genetic parallel programming (GPP). GPP is a novel genetic programming paradigm which evolves parallel program solutions. With the rapid development of lookup-table-based (LUT-based) field programmable gate arrays (FPGAs), traditional circuit design and optimisation techniques cannot fully exploit the LUTs in LUT-based FPGAs. Based on the GPP paradigm, we have developed a combinational logic circuit learning system, called GPP logic circuit synthesiser (GPPLCS), in which a multilogic-unit processor is used to evaluate LUT circuits. To show the effectiveness of the GPPLCS, we have performed a series of experiments to evolve combinational logic circuits with two- and four-input LUTs. In this paper, we present eleven multi-output Boolean problems and their evolved circuits. The results show that the GPPLCS can evolve more compact four-input LUT circuits than the well-known LUT-based FPGA synthesis algorithms.http://www.bibsonomy.org/bibtex/26db67a1ce4a28c119dffdf2f7c326f42/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00algorithms, Libraries genetic Digital programming, Deduplication, <span style="color:#555555;">Moises G. <a href="http://www.bibsonomy.org/author/{de Carvalho}">de Carvalho</a> and Marcos Andre <a href="http://www.bibsonomy.org/author/Goncalves">Goncalves</a> and Alberto H. F. <a href="http://www.bibsonomy.org/author/Laender">Laender</a> and Altigran S. <a href="http://www.bibsonomy.org/author/{da Silva}">da Silva</a> </span><em>Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '06, </em><em>page41--50. </em><em>Chapel Hill, NC, USA, </em><em>IEEE, </em><em>June2006. </em>Thu Jun 19 17:35:00 CEST 2008Chapel Hill, NC, USAProceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '06June41--50Learning to deduplicate2006algorithms, Libraries genetic Digital programming, Deduplication, Identifying record replicas in digital libraries and other types of digital repositories is fundamental to improve the quality of their content and services as well as to yield eventual sharing efforts. Several deduplication strategies are available, but most of them rely on manually chosen settings to combine evidence used to identify records as being replicas. In this paper, we present the results of experiments we have carried out with a novel machine learning approach we have proposed for the de duplication problem. This approach, based on genetic programming (GP), is able to automatically generate similarity functions to identify record replicas in a given repository. The generated similarity functions properly combine and weight the best evidence available among the record fields in order to tell when two distinct records represent the same real-world entity. The results of the experiments show that our approach outperforms the baseline method by Fellegi and Sunter by more than 12percent when identifying replicas in a data set containing researcher's personal data, and by more than 7percent, in a data set with article citation datahttp://www.bibsonomy.org/bibtex/2dd8dd90ebbe6902b6828dc013da54d56/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00function randomly equations, coverage, problem sizes, gates, input synthesis, population arbitrary test variables, size, circuits, digital experimental CAD, complete applicability, training set results, designed programming, sets, expressions, recognition optimization small genetic logic functions, criterion, algorithms, <span style="color:#555555;">Karen M. <a href="http://www.bibsonomy.org/author/Dill">Dill</a> and James H. <a href="http://www.bibsonomy.org/author/Herzog">Herzog</a> and Marek A. <a href="http://www.bibsonomy.org/author/Perkowski">Perkowski</a> </span><em>Communications, Computers and Signal Processing, PACRIM 1997, </em><em>2, </em><em>page823--826. </em><em>Victoria, BC, Canada, </em><em>20-22 August1997. </em>Thu Jun 19 17:35:00 CEST 2008Victoria, BC, CanadaCommunications, Computers and Signal Processing, PACRIM 199720-22 August823--826Genetic programming and its applications to the synthesis of digital logic21997function randomly equations, coverage, problem sizes, gates, input synthesis, population arbitrary test variables, size, circuits, digital experimental CAD, complete applicability, training set results, designed programming, sets, expressions, recognition optimization small genetic logic functions, criterion, algorithms, Genetic programming is applied to the synthesis of arbitrary logic expressions. As a new method of logic synthesis, this technique is uniquely advantageous in its flexibility for both problem applicability and optimization criterion. A number of experiments were conducted exploring this method with different types of logic gates and population sizes. While complete function coverage is not guaranteed, the best experimental test results over eight randomly designed functions, of four to seven input variables, have produced logic equations with a 98.4% function coverage. In addition, the relation between the training set size for the genetic program and function coverage was also empirically explored. These experiments showed that only small training sets were necessary for function recognition.http://www.bibsonomy.org/bibtex/2002f844c77738f07d6aed27377358948/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00algorithms, Filters, genetic Evolvable Design, Electronic Digital Evolutionary Algorithms, programming, VHDL Hardware, <span style="color:#555555;">Massimiliano <a href="http://www.bibsonomy.org/author/Erba">Erba</a> and Roberto <a href="http://www.bibsonomy.org/author/Rossi">Rossi</a> and Valentino <a href="http://www.bibsonomy.org/author/Liberali">Liberali</a> and Andrea <a href="http://www.bibsonomy.org/author/Tettamanzi">Tettamanzi</a> </span><em>Genetic Programming, Proceedings of EuroGP'2001, </em><em>volume2038ofLNCS, </em><em>page36--50. </em><em>Lake Como, Italy, </em><em>Springer-Verlag, </em><em>18-20 April2001. </em>Thu Jun 19 17:35:00 CEST 2008Lake Como, ItalyGenetic Programming, Proceedings of EuroGP'200118-20 April36--50LNCSAn Evolutionary Approach to Automatic Generation of {VHDL} Code for Low-Power Digital Filters20382001algorithms, Filters, genetic Evolvable Design, Electronic Digital Evolutionary Algorithms, programming, VHDL Hardware, An evolutionary algorithm is used to design a finite impulse response digital filter with reduced power consumption. The proposed design approach combines genetic optimization and simulation methodology, to evaluate a multi-objective fitness function which includes both the suitability of the filter transfer function and the transition activity of digital blocks. The proper choice of fitness function and selection criteria allows the genetic algorithm to perform a better search within the design space, thus exploring possible solutions which are not considered in the conventional structured design methodology. Although the evolutionary process is not guaranteed to generate a filter fully compliant to specifications in every run, experimental evidence shows that, when specifications are met, evolved filters are much better than classical designs both in terms of power consumption and in terms of area, while maintaining the same performance.http://www.bibsonomy.org/bibtex/29c366678ba4021fb7010a35f73b33145/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00genetic algorithms, simulated neural digital annealing, processing, signal programming, networks <span style="color:#555555;">K. C. <a href="http://www.bibsonomy.org/author/Sharman">Sharman</a> and A. I. <a href="http://www.bibsonomy.org/author/Esparcia-Alcazar">Esparcia-Alcazar</a> and Y. <a href="http://www.bibsonomy.org/author/Li">Li</a> </span><em>Technical Report, </em><em>CSC-95012. </em><em>Faculty of Engineering, </em><em>Glasgow G12 8QQ, Scotland, </em><em>31 March1995. </em>Thu Jun 19 17:35:00 CEST 2008Glasgow G12 8QQ, Scotland31 MarchCSC-95012Evolving Digital Signal Processing Algorithms by Genetic ProgrammingTechnical Report1995genetic algorithms, simulated neural digital annealing, processing, signal programming, networks We introduce a novel genetic programming (GP) technique to evolve both the structure and parameters of adaptive digital signal processing algorithms. This is accomplished by defining a set of node functions and terminals to implement the basic operations commonly used in a large class of DSP algorithms. In addition, we show how simulated annealing may be employed to assist the GP in optimising the numerical parameters of expression trees. The concepts are illustrated by using GP to evolve high performance algorithms for detecting binary data sequences at the output of a noisy, non-linear communications channel.http://www.bibsonomy.org/bibtex/2776025f0fab56f8e987d2332ca977c8d/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00programming, Neural annealing, genetic algorithms, Digital Signal Networks, Simulated Processing Recurrent <span style="color:#555555;">Anna I. <a href="http://www.bibsonomy.org/author/Esparcia-Alcazar">Esparcia-Alcazar</a> and Ken C. <a href="http://www.bibsonomy.org/author/Sharman">Sharman</a> </span><em>Technical Report, </em><em>CSC-96009. </em><em>Faculty of Engineering, </em><em>Glasgow G12 8QQ, Scotland, </em>(<em>1996</em>)Thu Jun 19 17:35:00 CEST 2008Glasgow G12 8QQ, ScotlandCSC-96009Evolving Recurrent Neural Network Architectures by Genetic ProgrammingTechnical Report1996programming, Neural annealing, genetic algorithms, Digital Signal Networks, Simulated Processing Recurrent We propose a novel design paradigm for recurrent neural networks. This employs a two-stage Genetic Programming / Simulated Annealing hybrid algorithm to produce a neural network which satisfies a set of design constraints. The Genetic Programming part of the algorithm is used to evolve the general topology of the network, along with specifications for the neuronal transfer functions, while the Simulated Annealing component of the algorithm adapts the network's connection weights in response to a set of training data. Our approach offers important advantages over existing methods for automated network design. Firstly, it allows us to develop recurrent network connections; secondly, we are able to employ neurons with arbitrary transfer functions, and thirdly, the approach yields an efficient and easy to implement on-line training algorithm. The procedures involved in using the GP/SA hybrid algorithm are illustrated by using it to design a neural network for adaptive filtering in a signal processing application.http://www.bibsonomy.org/bibtex/2d2a0d9a90a8d2ae9fa108f5773f0b93c/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00Processing Simulated Signal Digital algorithms, Adaptive programming, Annealing, genetic Filtering <span style="color:#555555;">Anna I. <a href="http://www.bibsonomy.org/author/Esparcia-Alcazar">Esparcia-Alcazar</a> and Ken C. <a href="http://www.bibsonomy.org/author/Sharman">Sharman</a> </span><em>Technical Report, </em><em>CSC-96010. </em><em>Faculty of Engineering, </em><em>Glasgow G12 8QQ, Scotland, </em>(<em>1996</em>)Thu Jun 19 17:35:00 CEST 2008Glasgow G12 8QQ, ScotlandCSC-96010Application of Genetic Programming to Signal Processing ProblemsTechnical Report1996Processing Simulated Signal Digital algorithms, Adaptive programming, Annealing, genetic Filtering The field of Digital Signal Processing (DSP) is concerned with the restoration of signals which have undergone distortion and interference or noise corruption as a result of being transmitted. The usual way to recover such a signal is by adaptive filtering. Designing adaptive filters is not an easy task. It usually involves complicated algorithms whose performance depends on the skill of the designer as well as the power of the computer used. The aim of the present work is to provide a way of automating such process by means of a black box technique. In this approach, both the structure and the parameters of adaptive filters are evolved. The former is done by Genetic Programming (GP) and the latter is done by Simulated Annealing (SA). The power of the hybrid GP/SA is demonstrated with some results on three interesting DSP applications: channel equalisation, noise cancellation and interference removal.http://www.bibsonomy.org/bibtex/2facc1d2a5d9f5d6e7b1f163b13f48974/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00fault circuits, noise evolvable hardware, robustness tolerance, digital <span style="color:#555555;">Morten <a href="http://www.bibsonomy.org/author/Hartmann">Hartmann</a> and Frode <a href="http://www.bibsonomy.org/author/Eskelund">Eskelund</a> and Pauline C. <a href="http://www.bibsonomy.org/author/Haddow">Haddow</a> and Julian F. <a href="http://www.bibsonomy.org/author/Miller">Miller</a> </span><em>GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, </em><em>page171--177. </em><em>New York, </em><em>Morgan Kaufmann Publishers, </em><em>9-13 July2002. </em>Thu Jun 19 17:35:00 CEST 2008New YorkGECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference9-13 July171--177Evolving Fault Tolerance On An Unreliable Technology Platform2002fault circuits, noise evolvable hardware, robustness tolerance, digital http://www.bibsonomy.org/bibtex/286411f726a7156f64a7ff773e0921718/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00test digital generation circuits, evolving problem, techniques, evolutionary design, genetic table vector VLSI, truth testing, algorithms, logic <span style="color:#555555;">Kosuke <a href="http://www.bibsonomy.org/author/Imamura">Imamura</a> and James A. <a href="http://www.bibsonomy.org/author/Foster">Foster</a> and Axel W. <a href="http://www.bibsonomy.org/author/Krings">Krings</a> </span><em>The Second NASA/DoD workshop on Evolvable Hardware, </em><em>page75--80. </em><em>Palo Alto, California, </em><em>IEEE Computer Society, </em><em>13-15 July2000. </em>Thu Jun 19 17:35:00 CEST 2008Palo Alto, CaliforniaThe Second NASA/DoD workshop on Evolvable Hardware13-15 July75--80The Test Vector Problem and Limitations to Evolving Digital Circuits2000test digital generation circuits, evolving problem, techniques, evolutionary design, genetic table vector VLSI, truth testing, algorithms, logic Evolvable Hardware (EHW) has been proposed as a new technique to design complex systems. Often, complex systems turn out to be very difficult to evolve. The problem is that a general strategy is too difficult for the evolution process to discover directly. This paper proposes a new approach that performs incremental evolution in two directions: from complex system to sub-systems and from subsystems back to complex system. In this approach, incremental evolution gradually decomposes a complex problem into some sub-tasks. In a second step, we gradually make the tasks more challenging and general. Our approach automatically discovers the sub-tasks, their sequence as well as circuit layout dimensions. Our method is tested in a digital circuit domain and compared to direct evolution. We show that our bidirectional incremental approach can handle more complex, harder tasks and evolve them more effectively, then direct evolution.http://www.bibsonomy.org/bibtex/2890afca881de545b0a26c1bf83f56ea6/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00dynamic real-time, programming, evolution pheromone, agents, adaptiv, digital applications, pbs, search, algorithms, genetic emergence, strategies, flies, population-based <span style="color:#555555;">H. <a href="http://www.bibsonomy.org/author/{Van Dyke Parunak}">Van Dyke Parunak</a> </span><em>Genetic Programming Theory and Practice III, </em><em>volume9ofGenetic Programming, </em><em>chapter 2, </em><em>Springer, </em><em>Ann Arbor, </em><em>12-14 May2005. </em>Thu Jun 19 17:35:00 CEST 2008Ann ArborGenetic Programming Theory and Practice {III}212-14 May15--32Genetic ProgrammingEvolving Swarming Agents in Real Time92005dynamic real-time, programming, evolution pheromone, agents, adaptiv, digital applications, pbs, search, algorithms, genetic emergence, strategies, flies, population-based http://www.bibsonomy.org/bibtex/23323cf81b527356bcb09651e618c32e5/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00second-order S-expressions, defined low IIR algorithms, functions, sensitivity genetic coefficient digital automatically filters, magnitude measure, filter method, fitness synthesis programming, filter, sensitivity, ADF <span style="color:#555555;">Kazuyoshi <a href="http://www.bibsonomy.org/author/Uesaka">Uesaka</a> and Masayuki <a href="http://www.bibsonomy.org/author/Kawamata">Kawamata</a> </span><em>IEEE Signal Processing Letters</em><em>7(4):83--85</em><em>April2000. </em>Thu Jun 19 17:35:00 CEST 2008IEEE Signal Processing LettersApril483--85Synthesis of low-sensitivity second-order digital filters using genetic programming with automatically defined functions72000second-order S-expressions, defined low IIR algorithms, functions, sensitivity genetic coefficient digital automatically filters, magnitude measure, filter method, fitness synthesis programming, filter, sensitivity, ADF This letter proposes a synthesis method for low coefficient sensitivity second-order IIR digital filter structures using genetic programming with automatically defined functions (GP-ADF). In this letter, digital filter structures are represented as S-expressions with subroutines. It is easy to generate syntactically valid S-expressions and perform the genetic operations, because the representation is suitable for GP. A numerical example uses the fitness measure, including the magnitude sensitivity, and demonstrates that the proposed method can synthesize efficiently very low coefficient sensitivity filter structures.