Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques and Applications

Langdon, William B.; Poli, Riccardo; McPhee, Nicholas Freitag; Koza, John R.

doi:10.1007/978-3-540-78293-3_22

Cited by 84 publications

(49 citation statements)

References 216 publications

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“…This stage of the method contains the preparation steps for the program execution process [11,12,18]. The first step is the function set Φ definition.…”

Section: Initialization Of Preparation Stepsmentioning

confidence: 99%

“…The first step is the function set Φ definition. In the modified hGP, the Φ set has two subsets for the arithmetic and mathematical functions, Φ and Φ , respectively, [18]. So, Φ = {Φ , Φ }, where Φ = {+, −, * , /} a nd Φ = {exp, log, sin, cos, Logistic, Gompertz I, Gompertz II, Bass, Bi-Logistic, LogInLog}.…”

Section: Initialization Of Preparation Stepsmentioning

confidence: 99%

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Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries

Salpasaranis

Stylianakis²,

Kotsopoulos³

2014

Advances in Operations Research

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This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers' population. The method constitutes an expansion of the hybrid Genetic Programming (hGP) method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and LogInLog. In addition, the aforementioned functions and models expand the function set of hGP. The application of the method in combination with macroeconomic indicators such as Gross Domestic Product per Capita (GDPpC) and Consumer Prices Index (CPI) leads to the creation of forecasting models and scenarios for medium-and long-term level of predictability. The forecasting module of the program has also been improved with the multi-levelled use of the statistical indices as fitness functions and model selection indices. The implementation of the modified-hGP in the datasets of mobile subscribers in the Organisation for Economic Cooperation and Development (OECD) countries shows very satisfactory forecasting performance.

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“…This stage of the method contains the preparation steps for the program execution process [11,12,18]. The first step is the function set Φ definition.…”

Section: Initialization Of Preparation Stepsmentioning

confidence: 99%

Section: Initialization Of Preparation Stepsmentioning

confidence: 99%

Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries

Salpasaranis

Stylianakis²,

Kotsopoulos³

2014

Advances in Operations Research

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“…Actually EC encompasses several variations on the above general principle, including Genetic Algorithms (GA), evolutionary programming, evolution strategies and Genetic Programming (GP) -the latter involves the evolution of computer programs, represented as syntax trees (Langdon et al 2008).…”

Section: Figure 4 -The Basic Steps In Evolutionary Computingmentioning

confidence: 99%

Computational Intelligence for Carbon-Centric Computing

Fulcher¹

2010

AJIS

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The focus of this paper is twofold: firstly we make a case for the use of Computational Intelligence (CI) techniques in the modelling and/or prediction of global weather, CO 2 emissions, climate change and similar endeavours. CI exploits processes found in Nature, albeit by way of software simulations on digital computers (i.e. in silico), and excel in particular at pattern recognition and/or classification. Moreover, they are characterized as being non-algorithmic, bottom-up, data-driven, and learn-by-example. The second focus of this paper is to propose the use of carbonrather than silicon-based computing, specifically in the form of DNA (or molecular) computing. Notwithstanding the unsolved difficulties with the latter (especially concerning Input/Output), its inherent massive parallelism has the potential to yield significant performance advantages. Finally, to come full circle, it could well eventuate that the inherent parallelism of DNA Computing could be brought to bear in the modelling/prediction endeavours mentioned previously.

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“…[21][29] [37]. Image enhancement is an important approach in digital image processing, which is a problem-oriented procedure.…”

Section: Introductionmentioning

confidence: 99%

Morphological image enhancement procedure design by using genetic programming

Wang

Tan

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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In this paper, we propose a genetic programming algorithm to design the morphological image enhancement procedure. Given a group of morphological operations and logical operations as function set, this algorithm evolves to produce a rational procedure which can enhance the input images. A novel mechanism which combines the ground truth method and feature significance is brought forward to evaluate the performance of images enhanced by generated procedures. In each generation, the best fitted individuals are selected on the basis of fitness values, and some individuals participate in crossover or mutation with a probability. After each generation, this algorithm outputs the best individual. Seven morphological operations and five logical operations are used in this algorithm. Furthermore, the structuring elements of morphological operations are randomly generated and varied in the whole pattern space. These methods promote the expressive ability of generated procedures. Examined by the binary image feature extraction, the procedure generated by this algorithm is more accurate and intelligible than previous work. In the task of gray scale image enhancement, the generated procedure is applied to infrared finger vein images to enhance the region of interest. More accurate features are extracted and the accuracy of authentication is promoted.

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Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques and Applications

Cited by 84 publications

References 216 publications

Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries

Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries

Computational Intelligence for Carbon-Centric Computing

Morphological image enhancement procedure design by using genetic programming

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