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检索条件"主题词=Cartesian Genetic Programming"

共 248 条记录，以下是41-50 订阅

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An Orthogonal cartesian genetic programming Algorithm for Evolvable Hardware

An Orthogonal Cartesian Genetic Programming Algorithm for Ev...

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International Conference on Identification, Information and Knowledge in the Internet of Things

作者： Ni, Fuchuan Li, Yuanxiang Yang, Xiaoyan Ni, Fuchuan Xiang, Jinhai Wuhan Univ State Key Lab Software Engn Wuhan Peoples R China Huazhong Agr Univ Dept Comp Sci Coll Informat Wuhan Peoples R China

ISBN: (纸本)9781479980031

Evolvable hardware (EHW) is facing the problems of scalability. Evolutionary algorithms often trap into local optima, or stalling in the later procedure. This paper analyses the difficulty of EHW. To improve the efficiency of cartesian genetic programming (CGP), Neighborhood searching and orthogonal experiment design are tailed to an orthogonal mutation operator and a new Orthogonal cartesian genetic programming algorithm is proposed. Demonstrated by experiments on the benchmark, the proposed Orthogonal cartesian genetic programming can jump out of Local optima and decrease the stalling effect.

关键词： Evolvable hardware cartesian genetic programming orthogonal experiment design Evolutionary algorithm

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Human Activity Recognition Using Parallel cartesian genetic programming

Human Activity Recognition Using Parallel Cartesian Genetic ...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Silva, Bruno M. P. Bernardino, Heder S. Barbosa, Helio J. C. Univ Fed Juiz de Fora Dept Comp Sci Juiz De Fora Brazil LNCC MCTIC Petropolis RJ Brazil Univ Fed Juiz de Fora Juiz De Fora Brazil

ISBN: (纸本)9781728183923

Human activity recognition (HAR) is applicable to a wide range of real-life situations. While machine learning algorithms can be applied for solving this problem, difficulties remain, such as handling a large amount of data available for training and selecting the most appropriate features. Hence, the advent of methods to reduce these issues and improve the currently available algorithms is relevant. Thus, we propose here the application of cartesian genetic programming of Artificial Neural Networks (CGPANN) for training models for HAR. As the computational cost is a relevant issue in this context, high-performance computing strategies in graphic processing units (GPU) are proposed for CGPANN. Two computational experiments are executed and the results show a decrease in computational time spent with the usage of different data structures for the parallel CGPANN on the GPU. Moreover, the CGPANN models for HAR are promising when compared to results from the literature.

关键词： Activity Recognition cartesian genetic programming Machine Learning Parallel Computing GPU

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A Co-evolutionary cartesian genetic programming with Adaptive Knowledge Transfer

A Co-evolutionary Cartesian Genetic Programming with Adaptiv...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Zhong, Jinghui Li, Linhao Liu, Wei-Li Feng, Liang Hu, Xiao-Min South China Univ Technol Sch Comp Sci & Engn Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou Guangdong Peoples R China Chongqing Univ Coll Comp Sci Chongqing Peoples R China Guangdong Univ Technol Sch Comp Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781728121536

cartesian genetic programming (CGP) is a powerful and popular tool for automatic generation of computer programs to solve user defined tasks. This paper proposes a Co-evolutionary CGP (named Co-CGP) which can automatically gain high-order knowledge to accelerate the search. In the Co-CGP, two modules are working in cooperation to solve a given problem. One module focuses on solving a series of small scale problems of the same type to generate the building blocks. Simultaneously, the second module focuses on combing the available building blocks to construct the final solution. Besides, an adaptive control strategy is introduced to automatically evaluate the effectiveness of the building blocks and adjust the search behaviour adaptively so as to improve search efficiency. The proposed Co-CGP is tested on eight problems with different complexities. Experimental results show that the Co-CGP can significantly improve the performance of CGP, in terms of both search efficiency and accuracy.

关键词： cartesian genetic programming Co-evolutionary Algorithm Transfer Learning Evolutionary Computation

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Phenotypic Duplication and Inversion in cartesian genetic programming applied to Boolean Function Learning 22

Phenotypic Duplication and Inversion in Cartesian Genetic Pr...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Kalkreuth, Roman TU Dortmund Univ Dept Comp Sci Dortmund North Rhine Wes Germany

ISBN: (纸本)9781450392686

The search performance of cartesian genetic programming (CGP) relies to a large extent on the sole use of genotypic point mutation in combination with extremely large redundant genotypes. Over the last years, steps have been taken to extend CGP's variation mechanisms by the introduction of advanced methods for recombination and mutation. One branch of these contributions addresses phenotypic variation in CGP. However, recent studies have demonstrated the limitations of phenotypic recombination in Boolean function learning and highlighted the effectiveness of the mutationonly approach. Therefore, in this work, we further explore phenotypic mutation in CGP by the introduction and evaluation of two phenotypic mutation operators that are inspired by chromosomal rearrangement. Our initial findings show that the proposed methods can significantly improve the search performance of CGP on various single- and multiple-output Boolean function benchmarks.

关键词： cartesian genetic programming Mutation Duplication Inversion Boolean Function Learning

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A Comprehensive Study on Subgraph Crossover in cartesian genetic programming 12th

A Comprehensive Study on Subgraph Crossover in Cartesian Gen...

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12th International Joint Conference on Computational Intelligence (IJCCI) / 12th International Conference on Evolutionary Computation Theory and Applications (ECTA)

作者： Kalkreuth, Roman TU Dortmund Univ Dept Comp Sci Otto Hahn Str 14 Dortmund Germany

ISBN: (纸本)9789897584756

While tree-based genetic programming is often used with crossover, cartesian genetic programming (CGP) is mostly used only with mutation as the sole genetic operator. In contrast to comprehensive and fundamental knowledge about crossover in tree-based GP, the state of knowledge in CGP appears to be still ambiguous and ambivalent. Two decades after CGP was officially introduced, the role of recombination in CGP is still considered to be an open and remaining question. Although some promising steps have been taken in the last years, comprehensive studies are needed to evaluate the role of crossover in CGP on a large set of problems. In this paper, we take a step forward on the crossover issue by comparing algorithms that utilize the subgraph crossover technique which has been proposed for CGP to the traditional mutation-only CGP. Experiments on well-known symbolic regression and Boolean function problems demonstrate that the use of algorithms that utilize the subgraph crossover outperform the mutation-only CGP on well-known benchmark problems.

关键词： cartesian genetic programming Crossover Subgraph

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Asynchronous Parallel cartesian genetic programming 17

Asynchronous Parallel Cartesian Genetic Programming

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genetic and Evolutionary Computation Conference (GECCO)

作者： Harter, Adam Tauritz, Daniel R. Siever, William M. Missouri Univ Sci & Technol Dept Comp Sci Nat Computat Lab Rolla MO 65401 USA Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

ISBN: (纸本)9781450349390

The run-time of evolutionary algorithms (EAs) is typically dominated by fitness evaluation. This is particularly the case when the genotypes are complex, such as in genetic programming (GP). Evaluating multiple offspring in parallel is appropriate in most types of EAs and can reduce the time incurred by fitness evaluation proportional to the number of parallel processing units. The most naive approach maintains the synchrony of evolution as employed by the vast majority of EAs, requiring an entire generation to be evaluated before progressing to the next generation. Heterogeneity in the evaluation times will degrade the performance, as parallel processing units will idle until the longest evaluation has completed. Asynchronous parallel evolution mitigates this bottleneck and techniques which experience high heterogeneity in evaluation times, such as cartesian GP (CGP), are prime candidates for asynchrony. However, due to CGP's small population size, asynchrony has a significant impact on selection pressure and biases evolution towards genotypes with shorter execution times, resulting in poorer results compared to their synchronous counterparts. This paper: 1) provides a quick introduction to CGP and asynchronous parallel evolution, 2) introduces asynchronous parallel CGP, and 3) shows empirical results demonstrating the potential for asynchronous parallel CGP to outperform synchronous parallel CGP.

关键词： genetic programming Asynchronous Parallel Evolution cartesian genetic programming Evolutionary Computing

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Iterative cartesian genetic programming: Creating General Algorithms for Solving Travelling Salesman Problems 19th

Iterative Cartesian Genetic Programming: Creating General Al...

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19th European Conference on genetic programming (EuroGP)

作者： Ryser-Welch, Patricia Miller, Julian F. Swan, Jerry Trefzer, Martin A. Univ York Heslington England

ISBN: (纸本)9783319306674;9783319306681

Evolutionary algorithms have been widely used to optimise or design search algorithms, however, very few have considered evolving iterative algorithms. In this paper, we introduce a novel extension to cartesian genetic programming that allows it to encode iterative algorithms. We apply this technique to the Traveling Salesman Problem to produce human-readable solvers which can be then be independently implemented. Our experimental results demonstrate that the evolved solvers scale well to much larger TSP instances than those used for training.

关键词： Iterative algorithms cartesian genetic programming TSP

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Short-Term Load Forecasting using cartesian genetic programming: an Efficient Evolutive Strategy Case: Australian electricity market 41

Short-Term Load Forecasting using Cartesian Genetic Programm...

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41st Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Giacomato, Francisco Sala, Enric Kampouropoulos, Konstantinos Romeral, Luis Univ Politecn Cataluna Dept Elect MCIA Ctr Terrassa Spain

ISBN: (纸本)9781479917624

Currently, the cartesian genetic programming approaches applied to regression problems tackle the evolutive strategy from a static point of view. They are confident on the evolving capacity of the genetic algorithm, with less attention being paid over alternative methods to enhance the generalization error of the trained models or the convergence time of the algorithm. On this article, we propose a novel efficient strategy to train models using cartesian genetic programming at a faster rate than its basic implementation. This proposal achieves greater generalization and enhances the error convergence. Finally, the complete methodology is tested using the Australian electricity market as a case study.

关键词： short-term load forecast cartesian genetic programming evolutive strategy generalization error convergence time

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A comparison of cartesian genetic programming and Linear genetic programming

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11th European Conference on genetic programming

作者： Wilson, Garnett Banzhaf, Wolfgang Mem Univ Newfoundland St John NF Canada

ISBN: (纸本)9783540786702

Two prominent genetic programming approaches are the graph-based cartesian genetic programming (CGP) and Linear genetic programming (LGP). Recently, a formal algorithm for constructing a directed acyclic graph (DAG) from a classical LGP instruction sequence has been established. Given graph-based LGP and traditional CGP, this paper investigates the similarities and differences between the two implementations, and establishes that the significant difference between them is each algorithm's means of restricting inter-connectivity of nodes. The work then goes on to compare the performance of two representations each (with varied connectivity) of LGP and CGP to a directed cyclic graph (DCG) GP with no connectivity restrictions on a medical classification and regression benchmark.

关键词： Linear genetic programming cartesian genetic programming

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Compact Version of cartesian genetic programming 19

Compact Version of Cartesian Genetic Programming

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19th International Conference on Applied Electronics (AE)

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

ISBN: (纸本)9788026102762

This paper deals with the design of the compact version of cartesian genetic programming. The focus is given to the search algorithm of type (1+1). The paper presents the approach that detects changes in the phenotype and, based on that, the algorithm can omit the evaluation of a candidate solution. The author uses the evolutionary design of multipliers as benchmark to present the efficiency of the algorithm.

关键词： cartesian genetic programming CGP Evolutionary design Fitness function

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