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检索条件"主题词=Cartesian Genetic Programming"

共 248 条记录，以下是61-70 订阅

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Non-photorealistic Rendering with cartesian genetic programming Using Graphics Processing Units 1

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7th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART) / 14th European Event on Biologically Inspired Music, Sound, Art and Design

作者： Bakurov, Illya Ross, Brian J. Brock Univ Dept Comp Sci 1812 Sir Isaac Brock Way St Catharines ON L2S 3A1 Canada

ISBN: (数字)9783319775838

ISBN: (纸本)9783319775838;9783319775821

A non-photorealistic rendering system implemented with cartesian genetic programming (CGP) is discussed. The system is based on Baniasadi's NPR system using tree-based GP. The CGP implementation uses a more economical representation of rendering expressions compared to the tree-based system. The system borrows their many-objective fitness evaluation scheme, which uses a model of aesthetics, colour testing, and image matching. GPU acceleration of the paint stroke application results in up to 6 times faster rendering times compared to CPU-based renderings. The convergence dynamics of CGP's mu+lambda evolutionary strategy was more unstable than conventional GP runs with large populations. One possible reason may be the sensitivity of the smaller mu+lambda population to the many-objective ranking scheme, especially when objectives are in conflict with each other. This instability is arguably an advantage as an exploratory tool, especially when considering the subjectivity inherent in evolutionary art.

关键词： Non-photorealistic rendering cartesian genetic programming Graphics processing units Evolutionary art

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On Heuristics for Seeding the Initial Population of cartesian genetic programming Applied to Combinational Logic Circuits

On Heuristics for Seeding the Initial Population of Cartesia...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Manfrini, Francisco A. L. Bernardino, Heder S. Barbosa, Helio J. C. Univ Fed Juiz de Fora Juiz de Fora MG Brazil IFET Juiz De Fora MG Brazil LNCC Petropolis RJ Brazil

ISBN: (纸本)9781450343237

The design of circuits is an important research field and the corresponding optimization problems are complex and computationally expensive. Here, a cartesian genetic programming (CGP) technique was used to design combinational logic circuits. Several configurations were tested for seeding the initial population. First, the number of rows, columns, and levels-back were varied. In addition, the initial population was generated using only NAND gates. These configurations were compared with results from the literature in four benchmark circuits, where in all instances it was possible to find that some seeding configurations contributed beneficially to the evolutionary process, allowing CGP to find a solution employing a lower number of fitness evaluations. Finally, the variation of the number of nodes of the individuals during the search was also analyzed and the results showed that there is a correlation between the topology of the initial population and the region of the search space which is explored.

关键词： cartesian genetic programming population seeding combinational logic circuits

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Behavior Control of Multiple Agents by cartesian genetic programming Equipped with Sharing Sub-programs among Agents 8

Behavior Control of Multiple Agents by Cartesian Genetic Pro...

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8th IEEE International Workshop on Computational Intelligence and Applications (IWCIA)

作者： Hara, Akira Kushida, Jun-ichi Okita, Tomoya Takahama, Tetsuyuki Hiroshima City Univ Grad Sch Informat Sci Asaminami Ku 3-4-1 Ozuka Higashi Hiroshima 7313194 Japan Hiroshima City Univ Fac Informat Sci Hiroshima Japan

ISBN: (纸本)9781479998869

In this paper, we focus on evolutionary optimization of multi-agent behavior. There are two representative models for multi-agent control, homogeneous and heterogeneous models. In the homogeneous model, all agents are controlled by the same controller. Therefore, it is difficult to realize complex cooperative behavior such as division of labors. In contrast, in the heterogeneous model, respective agents can play different roles for cooperative tasks. However, the search space becomes too large to optimize respective controllers. To solve the problems, we propose a new multi-agent control model based on cartesian genetic programming (CGP). In CGP, each individual represents a graph-structural program and it can have multiple outputs. The feature is utilized for controlling multiple agents in our model. In addition, we propose a new genetic operator dedicated to multi-agent control. Our method enables multiple agents to not only take different actions according to their own roles but also share sub-programs if the same behavior is needed for solving problems. We applied our method to a food foraging problem. The experimental results showed that the performance of our method is superior to those of the conventional models.

关键词： Evolutionary Computation genetic programming cartesian genetic programming Multi-Agent Systems

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Length Bias and Search Limitations in cartesian genetic programming 13

Length Bias and Search Limitations in Cartesian Genetic Prog...

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15th genetic and Evolutionary Computation Conference (GECCO)

作者： Goldman, Brian W. Punch, William F. Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA

ISBN: (纸本)9781450319638

In this paper we examine how cartesian genetic programming's (CGP's) method for encoding directed acyclic graphs (DAGs) and its mutation operator bias the effective length of individuals as well as the distribution of inactive nodes in the genome. We investigate these biases experimentally using two CGP variants as comparisons: Reorder, a method for shuffling node ordering without effecting individual evaluation, and DAG, a method for removing the concept of node position. Experiments were performed on four problems tailored to highlight potential search limitations, with further testing on the 3-bit multiplier problem. Unlike previous work, our experiments show that CGP has an innate parsimony pressure that makes it very difficult to evolve individuals with a high percentage of active nodes. This bias is particularly prevalent as the length of an individual increases. Furthermore, these problems are compounded by CGP's positional biases which can make some problems effectively unsolvable. Both Reorder and DAG appear to avoid these problems and outperform Normal CGP on preliminary benchmark testing. Finally, these new techniques require more reasonable genome sizes than those suggested in current CGP, with some evidence that solutions are also more terse.

关键词： cartesian genetic programming Neutrality Bloat

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Accelerating Convergence in cartesian genetic programming by Using a New genetic Operator 13

Accelerating Convergence in Cartesian Genetic Programming by...

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15th genetic and Evolutionary Computation Conference (GECCO)

作者： Meier, Andreas Gonter, Mark Kruse, Rudolf Volkswagen AG Res Grp D-38436 Wolfsburg Germany Univ Magdeburg Fac Comp Sci D-39114 Magdeburg Germany

ISBN: (纸本)9781450319638

genetic programming algorithms seek to find interpretable and good solutions for problems which are difficult to solve analytically. For example, we plan to use this paradigm to develop a car accident severity prediction model for new occupant safety functions. This complex problem will suffer from the major disadvantage of genetic programming, which is its high demand for computational effort to find good solutions. A main reason for this demand is a low rate of convergence. In this paper, we introduce a new genetic operator called forking to accelerate the rate of convergence. Our idea is to interpret individuals dynamically as centers of local Gaussian distributions and allow a sampling process in these distributions when populations get too homogeneous. We demonstrate this operator by extending the cartesian genetic programming algorithm and show that on our examples convergence is accelerated by over 50% on average. We finish this paper with giving hints about parameterization of the forking operator for other problems.

关键词： cartesian genetic programming optimization genetic operator

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Automatic Design of Sound Synthesizers as Pure Data Patches using Coevolutionary Mixed-typed cartesian genetic programming 14

Automatic Design of Sound Synthesizers as Pure Data Patches ...

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16th genetic and Evolutionary Computation Conference (GECCO)

作者： Macret, Matthieu Pasquier, Philippe Simon Fraser Univ Sch Interact Arts & Technol Burnaby BC V3T 0A3 Canada

ISBN: (纸本)9781450326629

A sound synthesizer can be defined as a program that takes a few input parameters and returns a sound. The general sound synthesis problem could then be formulated as: given a sound (or a set of sounds) what program and set of input parameters can generate that sound (set of sounds)? We propose a novel approach to tackle this problem in which we represent sound synthesizers using Pure Data (Pd), a graphic programming language for digital signal processing. We search the space of possible sound synthesizers using Co-evolutionary Mixed-typed cartesian genetic programming (MT-CGP), and the set of input parameters using a standard genetic Algorithm (GA). The proposed algorithm co-evolves a population of MT-CGP graphs, representing the functional forms of synthesizers, and a population of GA chromosomes, representing their inputs parameters. A fitness function based on the Mel-frequency Cepstral Coefficients (MFCC) evaluates the distance between the target and produced sounds. Our approach is capable of suggesting novel functional forms and input parameters, suitable to approximate a given target sound (and we hope in future iterations a set of sounds). Since the resulting synthesizers are presented as Pd patches, the user can experiment, interact with, and reuse them.

关键词： cartesian genetic programming Coevolution Sound Synthesis

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Visualisation and Analysis of genetic Records Produced by cartesian genetic programming

Visualisation and Analysis of Genetic Records Produced by Ca...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Sekanina, Lukas Kapusta, Vlastimil Brno Univ Technol Fac Informat Technol Ctr Excellence IT4Innovat Brno Czech Republic Brno Univ Technol Fac Informat Technol Brno Czech Republic

ISBN: (纸本)9781450343237

cartesian genetic programming (CGP) is a branch of genetic programming in which candidate designs are represented using directed acyclic graphs. Evolutionary circuit design is the most typical application of CGP. This paper presents a new software tool-CGPAnalyzer-developed to analyse and visualise a genetic record (i.e. a log file) generated by CGP-based circuit design software. CGPAnalyzer automatically finds key genetic improvements in the genetic record and presents relevant phenotypes. The comparison module of CGPAnalyzer allows the user to select two phenotypes and compare their structure, history and functionality. It thus enables to reconstruct the process of discovering new circuit designs. This feature is demonstrated by means of the analysis of the genetic record from a 9-parity circuit evolution. The CGPAnalyzer tool is a desktop application with a graphical user interface created using Java v.8 and Swing library.

关键词： cartesian genetic programming Digital circuit Visualisation

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Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems 06

Embedded Cartesian genetic programming and the lawnmower and...

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8th Annual genetic and Evolutionary Computation Conference

作者： Walker, James Alfred Miller, Julian Francis Univ York Dept Elect Intelligent Syst Grp York YO10 5DD N Yorkshire England

ISBN: (纸本)9781595931863

Embedded cartesian genetic programming (ECGP) is an extension of the directed graph based cartesian genetic programming (CGP), which is capable of automatically acquiring, evolving and re-using partial solutions in the form of modules. In this paper, we apply for the first time, CGP and ECGP to the well known Lawnmower problem and to the Hierarchical-if-and-Only-if problem. The latter is normally associated with genetic Algorithms. Computational effort figures are calculated from the results of both CGP and ECGP and our results compare favourably with other techniques.

关键词： cartesian genetic programming embedded cartesian genetic programming module acquisition automatically defined functions evolution lawnmower problem hierarchical-if-and-only-if

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On the Impact of the Objective Function on Imbalanced Data using cartesian genetic programming Neuroevolutionary Approaches

On the Impact of the Objective Function on Imbalanced Data u...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Melo Neto, Johnathan M. Bernardino, Heder S. Barbosa, Helio J. C. Univ Fed Juiz de Fora Juiz De Fora MG Brazil Univ Fed Juiz de Fora Dept Comp Sci Juiz De Fora MG Brazil LNCC MCTIC Petropolis RJ Brazil

ISBN: (纸本)9781728121536

The training of machine learning models for imbalanced data classification is a challenging task. Several metrics have been used to assess the performance of the classifiers. Each metric is appropriate for a class of problems, and some users often do not have a clear notion of which metric to use. In such cases, it is desirable that the chosen objective function provides good overall performance for most of the existing metrics. Here, three neuroevolutionary approaches based on cartesian genetic programming are used in order to investigate the impact of optimizing accuracy, G-mean, F-beta-score, and the area under the Receiver Operating Characteristic (ROC) curve when creating classifiers based on Artificial Neural Networks applied to imbalanced data classification problems. The results suggest that the optimization of G-mean and F-beta-score generate models that present a superior overall performance in all metrics.

关键词： imbalanced data classification neuroevolution cartesian genetic programming

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Fast Detection of Active Genes in cartesian genetic programming

Fast Detection of Active Genes in Cartesian Genetic Programm...

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International Conference on Signals and Electronic Systems (ICSES)

作者： Burian, Petr Univ W Bohemia Reg Innovat Ctr Elect Engn Plzen Czech Republic

ISBN: (纸本)9781479970094

This paper deals with the implementation of fast active genes detector by an FPGA device. The author introduces the modular structure that determines active genes in genotypes of cartesian genetic programming. The use of the detector is suitable if the l-back parameter takes value of 1 or 2. The paper also discusses timing performance. The introduced active genes detector can be used for the reduction of fitness calculations in cartesian genetic programming.

关键词： CGP cartesian genetic programming Active genes Evolutionary Circuit Design Evolvable Hardware FPGA

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