Grammar as an adaptive evolutionary product

are color categories the evolutionary product of their usefulness in communication, or is this an accidental benefit they give us? it is argued here that embodiment constraints on color categorization suggest that communication is an add-on at best. thus, the steels & belpaeme (s&b) model may be important in explaining coordination, but only at the margin. furthermore, the concentration on discrimination is questionable: coclassification is at least as important.

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4. Human language as an evolutionary product

Bio-Linguistics ◽

10.1075/z.113.05hum ◽

2002 ◽

pp. 123-161

Keyword(s):

Human Language ◽

Evolutionary Product

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A two-stage algorithm in evolutionary product unit neural networks for classification

Expert Systems with Applications ◽

10.1016/j.eswa.2010.07.028 ◽

2011 ◽

Vol 38 (1) ◽

pp. 743-754 ◽

Cited By ~ 13

Author(s):

Antonio J. Tallón-Ballesteros ◽

César Hervás-Martínez

Keyword(s):

Neural Networks ◽

Two Stage ◽

Evolutionary Product

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Product evolutionary design driven by environmental performance

Concurrent Engineering ◽

10.1177/1063293x18805200 ◽

2018 ◽

Vol 27 (1) ◽

pp. 40-56

Author(s):

Lei Zhang ◽

Chenxing Zheng ◽

Yu Zheng ◽

Haihong Huang ◽

Qingdi Ke

Keyword(s):

Environmental Performance ◽

Quotient Space ◽

Evolutionary Process ◽

Evolutionary Design ◽

Space Theory ◽

Design Constraints ◽

Evolutionary Method ◽

Operation Modes ◽

Better Than ◽

Evolutionary Product

This article is in terms of product environmental performance demand and proposes four structure evolutionary operation modes which include combined evolutionary method, decomposition evolutionary method, replacement evolutionary method, and material-changing evolutionary method to express the structure evolutionary process of products. Through the quotient space theory and proposed method combined with probability statistics, probability mapping from environmental performance to product structure is established and the evolutionary individuals with outstanding environmental performance are listed. Through the analysis to the specific conditions of the evolutionary individuals, the design constraints are extracted, and the objective function of environmental performance is established. This article presents an interactive genetic algorithm as evolutionary algorithm and combines it with four structure evolutionary operation modes to conduct corresponding gene manipulation and generates evolutionary product. Finally, the proposed methodology is successfully applied to engine gear chamber and the environmental impact is found to be better than before evolution.

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Evolutionary product development as a design tool

J of Design Research ◽

10.1504/jdr.2012.047920 ◽

2012 ◽

Vol 10 (3) ◽

pp. 141

Author(s):

Arthur O. Eger ◽

J.W. Drukker

Keyword(s):

Product Development ◽

Design Tool ◽

Evolutionary Product

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Evolutionary product unit based neural networks for hydrological time series analysis

Journal of Hydroinformatics ◽

10.2166/hydro.2010.099 ◽

2010 ◽

Vol 13 (4) ◽

pp. 825-841 ◽

Cited By ~ 6

Author(s):

Dulakshi S. K. Karunasingha ◽

A. W. Jayawardena ◽

W. K. Li

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Meteorological Data ◽

Time Series Prediction ◽

Network Models ◽

Engineering Analysis ◽

Neural Network Models ◽

Mlp Neural Network ◽

Evolutionary Product

Artificial Neural Networks (ANNs) are now widely used in many areas of science, medicine, finance and engineering. Analysis and prediction of time series of hydrological/and meteorological data is one such application. Problems that still exist in the application of ANN's are the lack of transparency and the expertise needed for training. An evolutionary algorithm-based method to train a type of neural networks called Product Units Based Neural Networks (PUNN) has been proposed in a 2006 study. This study investigates the applicability of this type of neural networks to hydrological time series prediction. The technique, with a few small changes to improve the performance, is applied to some benchmark time series as well as to a real hydrological time series for prediction. The results show that evolutionary PUNN produce more transparent models compared to widely used multilayer perceptron (MLP) neural network models. It is also seen that training of PUNN models requires less expertise compared to MLPs.

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