Cartesian genetic programming

Miller, Julian F.; Harding, Simon

doi:10.1145/1570256.1570428

Cited by 82 publications

(113 citation statements)

References 33 publications

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“…This applies not only to traditional forms of GP but also to Cartesian GP (CGP) [11,12], Grammatical Evolution (GE) [13,14], and Evolutionary Programming (EP) [15]. In fact, many of these challenges exist for most Evolutionary Algorithms (EAs).…”

Section: Introductionmentioning

confidence: 99%

Theoretical results in genetic programming: the next ten years?

Poli

Vanneschi

Langdon

et al. 2010

Genet Program Evolvable Mach

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We consider the theoretical results in GP so far and prospective areas for the future. We begin by reviewing the state of the art in genetic programming (GP) theory including: schema theories, Markov chain models, the distribution of functionality in program search spaces, the problem of bloat, the applicability of the no-free-lunch theory to GP, and how we can estimate the difficulty of problems before actually running the system. We then look at how each of these areas might develop in the next decade, considering also new possible avenues for theory, the challenges ahead and the open issues.

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Section: Introductionmentioning

confidence: 99%

Theoretical results in genetic programming: the next ten years?

Poli

Vanneschi

Langdon

et al. 2010

Genet Program Evolvable Mach

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“…The fitness function of MGGP is an error minimization problem. Then, parameters about MGGP are chosen by two ways, one way is that to use training group and validate group to decide and the other way is chosen on the basis of some suggested values [27][28][29][30][31][32][33][34][35][36][37][38][39][40], which are given in Table 3 …”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In the recent past, GP and its variants such as parisian genetic programming (PGP) [26,27], gene expression programming (GEP) [28], cartesian genetic programming (CGP) [29,30] and MGGP [31][32][33] have been successfully applied to various kinds of problems [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. Garg A et al [46][47][48][49][50] proposed several proved GP with machine learning can be applied into practical problems.…”

Section: Introductionmentioning

confidence: 99%

Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

Chen

Zeng

Jiang

et al. 2015

Neural Comput & Applic

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Complexity of analysis of landslide hazard is due to uncertainty. In this study, a novel approach multigene genetic programming based on separable functional network (MGGPSFN) is presented for predicting landslide displacement. Moreover, Pearson's cross-correlation coefficients and mutual information are adopted to look for the potential input variables for a forecast model in the paper. The performance of new model is verified through one case study in Baishuihe landslide in the Three Gorges Reservoir in China. In addition, we compared it with two methods, back-propagation neural network and radial basis function, and MGGPSFN got the best results in the same measurements.

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“…CGP is proposed by Miller and his colleagues [20], which has been widely used in the EHW community [16,22,28]. In this paper, the extrinsic EHW system is based on the CGP model.…”

Section: Cgp and The Chromosome Encodingmentioning

confidence: 99%

A three-step decomposition method for the evolutionary design of sequential logic circuits

Liang

Luo

Wang

2009

Genet Program Evolvable Mach

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Evolvable hardware (EHW) refers to an automatic circuit design approach, which employs evolutionary algorithms (EAs) to generate the configurations of the programmable devices. The scalability is one of the main obstacles preventing EHW from being applied to real-world applications. Several techniques have been proposed to overcome the scalability problem. One of them is to decompose the whole circuit into several small evolvable sub-circuits. However, current techniques for scalability are mainly used to evolve combinational logic circuits. In this paper, in order to decompose a sequential logic circuit, the state decomposition, output decomposition and input decomposition are united as a threestep decomposition method (3SD). A novel extrinsic EHW system, namely 3SD-ES, which combines the 3SD method with the (l, k) ES (evolution strategy), is proposed, and is used for the evolutionary designing of larger sequential logic circuits. The proposed extrinsic EHW system is tested extensively on sequential logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library. The results demonstrate that 3SD-ES has much better performance in terms of scalability. It enables the evolutionary designing of larger sequential circuits than have ever been evolved before.

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Cartesian genetic programming

Abstract: The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Cited by 82 publications

References 33 publications

Theoretical results in genetic programming: the next ten years?

Theoretical results in genetic programming: the next ten years?

Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

A three-step decomposition method for the evolutionary design of sequential logic circuits

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