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  • 1
    Publication Date: 2012-10-13
    Description: Franz Rothlauf: Design of Modern Heuristics Content Type Journal Article Category Book Review Pages 1-3 DOI 10.1007/s10710-012-9170-9 Authors Dario Landa-Silva, School of Computer Science, University of Nottingham, Nottingham, UK Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
    Published by Springer
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  • 2
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    Publication Date: 2012-04-12
    Description: Moshe Sipper: Evolved to Win Content Type Journal Article Category Book Review Pages 1-2 DOI 10.1007/s10710-012-9157-6 Authors Timothy Gosling, The Creative Assembly, Horsham, England Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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  • 3
    Publication Date: 2012-04-16
    Description:    Evolutionary Algorithms (EA) approach the genotype–phenotype relationship differently than does nature, and this discrepancy is a recurrent issue among researchers. Moreover, in spite of some performance improvements, it is a fact that biological knowledge has advanced faster than our ability to incorporate novel biological ideas into EAs. Recently, some researchers have started exploring computationally new comprehension of the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Gene Regulatory Network (ARN) model, by Wolfgang Banzhaf. Soon after some variants of the ARN were tested. In this paper, we describe one of those, the Regulatory Network Computational Device, demonstrating experimentally its capabilities. The efficacy and efficiency of this alternative is tested experimentally using typical benchmark problems for Genetic Programming (GP) systems. We devise a modified factorial problem to investigate the use of feedback connections and the scalability of the approach. In order to gain a better understanding about the reasons for the improved quality of the results, we undertake a preliminary study about the role of neutral mutations during the evolutionary process. Content Type Journal Article Pages 1-37 DOI 10.1007/s10710-012-9160-y Authors Rui L. Lopes, Center for Informatics and Systems of the University of Coimbra, Polo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal Ernesto Costa, Center for Informatics and Systems of the University of Coimbra, Polo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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  • 4
    Publication Date: 2012-04-17
    Description:    Evolvable Hardware has been a discipline for over 15 years. Its application has ranged from simple circuit design to antenna design. However, research in the field has often been criticised for not addressing real world problems. Intrinsic variability has been recognised as one of the major challenges facing the semiconductor industry. This paper describes an approach that optimises designs within a standard cell library by altering the transistor dimensions. The proposed approach uses a Multi-objective Genetic Algorithm to optimise the device widths within a standard cell. The designs are analysed using statistically enhanced transistor models (based on 3D-atomistic simulations) and statistical Spice simulations. The goal is to extract high-speed and low-power designs, which are more tolerant to the random fluctuations present in current and future technology nodes. The results show improvements in both the speed and power of the optimised standard cells and that the impact of threshold voltage variation is reduced. Content Type Journal Article Pages 235-256 DOI 10.1007/s10710-011-9131-8 Authors James Alfred Walker, Intelligent Systems Group, Department of Electronics, University of York, Heslington, York, YO10 5DD UK James A. Hilder, Intelligent Systems Group, Department of Electronics, University of York, Heslington, York, YO10 5DD UK Dave Reid, Device Modelling Group, Department of Electronics and Electrical Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow, G12 8LT UK Asen Asenov, Device Modelling Group, Department of Electronics and Electrical Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow, G12 8LT UK Scott Roy, Device Modelling Group, Department of Electronics and Electrical Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow, G12 8LT UK Campbell Millar, Device Modelling Group, Department of Electronics and Electrical Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow, G12 8LT UK Andy M. Tyrrell, Intelligent Systems Group, Department of Electronics, University of York, Heslington, York, YO10 5DD UK Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 3
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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  • 5
    Publication Date: 2012-04-17
    Description:    Many large combinatorial optimization problems tackled with evolutionary algorithms often require very high computational times, usually due to the fitness evaluation. This fact forces programmers to use clusters of computers, a computational solution very useful for running applications of intensive calculus but having a high acquisition price and operation cost, mainly due to the Central Processing Unit (CPU) power consumption and refrigeration devices. A low-cost and high-performance alternative comes from reconfigurable computing, a hardware technology based on Field Programmable Gate Array devices (FPGAs). The main objective of the work presented in this paper is to compare implementations on FPGAs and CPUs of different fitness functions in evolutionary algorithms in order to study the performance of the floating-point arithmetic in FPGAs and CPUs that is often present in the optimization problems tackled by these algorithms. We have taken advantage of the parallelism at chip-level of FPGAs pursuing the acceleration of the fitness functions (and consequently, of the evolutionary algorithms) and showing the parallel scalability to reach low cost, low power and high performance computational solutions based on FPGA. Finally, the recent popularity of GPUs as computational units has moved us to introduce these devices in our performance comparisons. We analyze performance in terms of computation times and economic cost. Content Type Journal Article Pages 403-427 DOI 10.1007/s10710-011-9137-2 Authors Juan A. Gomez-Pulido, Department of Technologies of Computers and Communications, University of Extremadura, 10003 Caceres, Spain Miguel A. Vega-Rodriguez, Department of Technologies of Computers and Communications, University of Extremadura, 10003 Caceres, Spain Juan M. Sanchez-Perez, Department of Technologies of Computers and Communications, University of Extremadura, 10003 Caceres, Spain Silvio Priem-Mendes, Department of Computer Science, School of Technology and Management, Polytechnic Institute of Leiria, 2410 Leiria, Portugal Vitor Carreira, Department of Computer Science, School of Technology and Management, Polytechnic Institute of Leiria, 2410 Leiria, Portugal Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 4
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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  • 6
    Publication Date: 2012-04-17
    Description:    A mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. A representation has high locality if most genotypic neighbours are mapped to phenotypic neighbours. Locality is seen as a key element in performing effective evolutionary search. It is believed that a representation that has high locality will perform better in evolutionary search and the contrary is true for a representation that has low locality. When locality was introduced, it was the genotype-phenotype mapping in bitstring-based Genetic Algorithms which was of interest; more recently, it has also been used to study the same mapping in Grammatical Evolution. To our knowledge, there are few explicit studies of locality in Genetic Programming (GP). The goal of this paper is to shed some light on locality in GP and use it as an indicator of problem difficulty. Strictly speaking, in GP the genotype and the phenotype are not distinct. We attempt to extend the standard quantitative definition of genotype-phenotype locality to the genotype-fitness mapping by considering three possible definitions. We consider the effects of these definitions in both continuous- and discrete-valued fitness functions. We compare three different GP representations (two of them induced by using different function sets and the other using a slightly different GP encoding) and six different mutation operators. Results indicate that one definition of locality is better in predicting performance. Content Type Journal Article Pages 365-401 DOI 10.1007/s10710-011-9136-3 Authors Edgar Galván-López, Natural Computing Research and Applications Group, University College Dublin, Dublin, Ireland James McDermott, Natural Computing Research and Applications Group, University College Dublin, Dublin, Ireland Michael O’Neill, Natural Computing Research and Applications Group, University College Dublin, Dublin, Ireland Anthony Brabazon, Natural Computing Research and Applications Group, University College Dublin, Dublin, Ireland Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 4
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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  • 7
    Publication Date: 2012-04-17
    Description:    EMBRACE has been proposed as a scalable, reconfigurable, mixed signal, embedded hardware Spiking Neural Network (SNN) device. EMBRACE, which is yet to be realised, targets the issues of area, power and scalability through the use of a low area, low power analogue neuron/synapse cell, and a digital packet-based Network on Chip (NoC) communication architecture. The paper describes the implementation and testing of EMBRACE-FPGA, an FPGA-based hardware SNN prototype. The operation of the NoC inter-neuron communication approach and its ability to support large scale, reconfigurable, highly interconnected SNNs is illustrated. The paper describes an integrated training and configuration platform and an on-chip fitness function, which supports GA-based evolution of SNN parameters. The practicalities of using the SNN development platform and SNN configuration toolset are described. The paper considers the impact of latency jitter noise introduced by the NoC router and the EMBRACE-FPGA processor-based neuron/synapse model on SNN accuracy and evolution time. Benchmark SNN applications are described and results demonstrate the evolution of high quality and robust solutions in the presence of noise. The reconfigurable EMBRACE architecture enables future investigation of adaptive hardware applications and self repair in evolvable hardware. Content Type Journal Article Pages 257-280 DOI 10.1007/s10710-011-9130-9 Authors Seamus Cawley, Bio-Inspired and Reconfigurable Computing Research Group, National University of Ireland, Galway, Galway, Ireland Fearghal Morgan, Bio-Inspired and Reconfigurable Computing Research Group, National University of Ireland, Galway, Galway, Ireland Brian McGinley, Bio-Inspired and Reconfigurable Computing Research Group, National University of Ireland, Galway, Galway, Ireland Sandeep Pande, Bio-Inspired and Reconfigurable Computing Research Group, National University of Ireland, Galway, Galway, Ireland Liam McDaid, Intelligent Systems Research Centre, University of Ulster, Magee Campus, Derry, Northern Ireland,UK Snaider Carrillo, Intelligent Systems Research Centre, University of Ulster, Magee Campus, Derry, Northern Ireland,UK Jim Harkin, Intelligent Systems Research Centre, University of Ulster, Magee Campus, Derry, Northern Ireland,UK Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 3
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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  • 8
    Publication Date: 2012-04-17
    Description:    In this paper we investigate using multi-objective genetic programming to evolve a feature extraction stage for multiple-class classifiers. We find mappings which transform the input space into a new, multi-dimensional decision space to increase the discrimination between all classes; the number of dimensions of this decision space is optimized as part of the evolutionary process. A simple and fast multi-class classifier is then implemented in this multi-dimensional decision space. Mapping to a single decision space has significant computational advantages compared to k -class-to-2-class decompositions; a key design requirement in this work has been the ability to incorporate changing priors and/or costs associated with mislabeling without retraining. We have employed multi-objective optimization in a Pareto framework incorporating solution complexity as an independent objective to be minimized in addition to the main objective of the misclassification error. We thus give preference to simpler solutions which tend to generalize well on unseen data, in accordance with Occam’s Razor. We obtain classification results on a series of benchmark problems which are essentially identical to previous, more complex decomposition approaches. Our solutions are much simpler and computationally attractive as well as able to readily incorporate changing priors/costs. In addition, we have also applied our approach to the KDD-99 intrusion detection dataset and obtained results which are highly competitive with the KDD-99 Cup winner but with a significantly simpler classification framework. Content Type Journal Article Pages 33-63 DOI 10.1007/s10710-011-9143-4 Authors Khaled Badran, Vision and Information Engineering Research Group, Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3D UK Peter Rockett, Vision and Information Engineering Research Group, Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3D UK Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 13 Journal Issue Volume 13, Number 1
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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  • 9
    Publication Date: 2012-04-17
    Description:    Computational tools for analyzing biochemical phenomena are becoming increasingly important. Recently, high-level formal languages for modeling and simulating biochemical reactions have been proposed. These languages make the formal modeling of complex reactions accessible to domain specialists outside of theoretical computer science. This research explores the use of genetic programming to automate the construction of models written in one such language. Given a description of desired time-course data, the goal is for genetic programming to construct a model that might generate the data. The language investigated is Kahramanoğullari’s and Cardelli’s Programming Interface for Modeling (PIM) language. The PIM syntax is defined in a grammar-guided genetic programming system. All time series generated during simulations are described by statistical feature tests, and the fitness evaluation compares feature proximity between the target and candidate solutions. PIM models of varying complexity were used as target expressions for genetic programming, and were successfully reconstructed in all cases. This shows that the compositional nature of PIM models is amenable to genetic program search. Content Type Journal Article Pages 3-31 DOI 10.1007/s10710-011-9144-3 Authors Brian J. Ross, Department of Computer Science, Brock University, 500 Glenridge Ave., St. Catharines, ON L2S 3A1, Canada Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 13 Journal Issue Volume 13, Number 1
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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  • 10
    Publication Date: 2012-04-17
    Description:    Evolutionary techniques may be applied to search for specific structures or functions, as specified in the fitness function. This paper addresses the challenge of finding an appropriate fitness function when searching for generic rather than specific structures which, when combined wiacteristic of defect tolerance on the circuit. Production defects for integrated circuits are expected to increase considerably. To avoid a corresponding drop in yield, improved defect tolerance solutions are needed. In the case of Field Programmable Gate Arrays (FPGAs), the pre-designed gate array provides a bridge between production and the application designers. Thus, introduction of defect tolerant techniques to the FPGA itself could provide a defect free gate array to the application designer, despite production defects. The search for defect tolerance presented herein is directed at finding defect tolerant structures for an important building block of FPGAs: Look-Up Tables (LUTs). Two key approaches are presented: (1) applying evolved generic building blocks to a traditional LUT design and (2) evolving the LUT design directly. The results highlight the fact that evolved generic defect tolerant structures can contribute to highly reliable circuit designs at the expense of area usage. Further, they show that applying such a technique, rather than direct evolution, has benefits with respect to evolvability of larger circuits, again at the expense of area usage. Content Type Journal Article Pages 281-303 DOI 10.1007/s10710-011-9129-2 Authors Asbjoern Djupdal, CRAB Lab, IDI, NTNU, Trondheim, Norway Pauline C. Haddow, CRAB Lab, IDI, NTNU, Trondheim, Norway Journal Genetic Programming and Evolvable Machines Online ISSN 1573-7632 Print ISSN 1389-2576 Journal Volume Volume 12 Journal Issue Volume 12, Number 3
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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