Genetic Programming of Logic-Based Neural Networks

2017 ◽  
pp. 213-226
Author(s):  
Vincent Charles Gaudet
Keyword(s):  
Neural Networks ◽  
Genetic Programming

scholarly journals Exploring genetic programming systems with MAP-Elites

2018 ◽  
Author(s):  
Emily Dolson ◽  
Alexander Lalejini ◽  
Charles Ofria
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Pareto Front ◽  
Underlying Structure ◽  
Phenotypic Traits ◽  
Computation Technique ◽  
Phenotype Space ◽  
Programming Systems ◽  
Insight Into ◽  
Connection Cost

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.

Using Genetic Programming to Extract Knowledge from Artificial Neural Networks

2008 ◽  
pp. 308-327
Author(s):  
Daniel Rivero ◽  
Miguel Varela ◽  
Javier Pereira
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Recurrent Neural Networks ◽  
Know How ◽  
Artificial Neural

A technique is described in this chapter that makes it possible to extract the knowledge held by previously trained artificial neural networks. This makes it possible for them to be used in a number of areas (such as medicine) where it is necessary to know how they work, as well as having a network that functions. This chapter explains how to carry out this process to extract knowledge, defined as rules. Special emphasis is placed on extracting knowledge from recurrent neural networks, in particular when applied in predicting time series.

Software Defect Prediction Using Genetic Programming and Neural Networks

2020 ◽  
pp. 1577-1597
Author(s):  
Mohammed Akour ◽  
Wasen Yahya Melhem
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Detailed Analysis ◽  
Software Quality ◽  
The Other ◽  
Defect Prediction ◽  
Data Repository ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Software Defect

This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.

A THREE-STEP COMBINED GENETIC PROGRAMMING AND NEURAL NETWORKS METHOD OF FORECASTING THE S&P/CASE-SHILLER HOME PRICE INDEX

2013 ◽  
Vol 12 (01) ◽  
pp. 1350001 ◽  
Author(s):  
MAK KABOUDAN ◽  
MARK CONOVER
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
San Francisco ◽  
Price Index ◽  
Combined Method ◽  
Second Stage ◽  
Third Stage ◽  
Multi Agent ◽  
Stage Process ◽  
Two Stages

Forecasts of the San Diego and San Francisco S&P/Case-Shiller Home Price Indices through December 2012 are obtained using a multi-agent system that utilizes January, 2002–June, 2011 data. Agents employ genetic programming (GP) and neural networks (NN) in a three-stage process to produce fits and forecasts. First, GP and NN compete to provide independent predictions. In the second stage, they cooperate by fitting the first-stage competitor's residuals. Outputs from the first two stages then become inputs to produce two final GP and NN outputs. The NN output from the third stage using the combined method produces improved forecasts over the 3-stage GP method as well as those produced by either method alone. The proposed methodology serves as an example of how combining more than one estimation/forecasting technique may lead to more accurate forecasts.

EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS

2005 ◽  
Vol 01 (01) ◽  
pp. 79-107 ◽  
Author(s):  
MAK KABOUDAN
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Random Walk ◽  
Genetic Programming ◽  
Exchange Rates ◽  
Us Dollar ◽  
Artificial Neural ◽  
Japanese Yen ◽  
The One

Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

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