Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming

2019 ◽  
Vol 27 (3) ◽  
pp. 497-523 ◽  
Author(s):  
Michaela Drahosova ◽  
Lukas Sekanina ◽  
Michal Wiglasz
Keyword(s):  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Filter Design ◽  
Training Data ◽  
Cartesian Genetic Programming ◽  
Design Problems ◽  
Quality Of Results ◽  
Regression Problems ◽  
Time Required

In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.

scholarly journals Sistem Deteksi Biometrik Keunikan Wajah Secara Real Time

2020 ◽  
Vol 4 (1) ◽  
pp. 30
Author(s):  
Anita Sindar RM Sinaga
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Filter Design ◽  
Detection System ◽  
Training Data ◽  
Gabor Wavelet ◽  
Design Problems ◽  
Camshift Algorithm ◽  
The Face ◽  
The Difference

<table width="605" border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="382"><p>Facial recognition is important for identifying a person's biodata profile. The physical development of students from the time they entered college to graduation has experienced inconspicuous changes but it is sometimes difficult to identify faces one by one. Digital form is becoming a trend to remember more real time. An important part of human physical identification has begun to shift from signature - finger - face selection. The face includes five important senses that are interconnected into an identification device. In this study the focus is on face detection based on color, the application of the Camshift Algorithm and finding the distance between the face sensing points is the result of the Gabor Wavelet method. Training data uses 4-8 second real time video. The hue histogram is basically the same as the RGB histogram, the difference is that the hue histogram uses the Hue value instead of RGB because the hue value represents natural color without regard to lighting. Gabor Wavelet transform is provided to solve filter design problems. The face detection system looks for face points to form a frame-shaped face selection if previously the face has been stored in a database so the system can easily describe biodata. Face selection can be done on live testing data. The selection box detection follows every facial movement.</p></td></tr></tbody></table>

scholarly journals FMCGP: frameshift mutation cartesian genetic programming

2021 ◽  
Author(s):  
Wei Fang ◽  
Mindan Gu
Keyword(s):  
Genetic Programming ◽  
Frameshift Mutation ◽  
Population Diversity ◽  
Search Performance ◽  
Mutation Operator ◽  
Cartesian Genetic Programming ◽  
Dna Mutation ◽  
Regression Problems ◽  
The Individual ◽  
Acyclic Directed Graph

AbstractCartesian Genetic Programming (CGP) is a variant of Genetic Programming (GP) with the individuals represented by a two-dimensional acyclic directed graph, which can flexibly encode many computing structures. In general, CGP only uses a point mutation operator and the genotype of an individual is of fixed size, which may lead to the lack of population diversity and then cause the premature convergence. To address this problem in CGP, we propose a Frameshift Mutation Cartesian Genetic Programming (FMCGP), which is inspired by the DNA mutation mechanism in biology and the frameshift mutation caused by insertion or deletion of nodes is introduced to CGP. The individual in FMCGP has variable-length genotype and the proposed frameshift mutation operator helps to generate more diverse offspring individuals by changing the compiling framework of genotype. FMCGP is evaluated on the symbolic regression problems and Even-parity problems. Experimental results show that FMCGP does not exhibit the bloat problem and the use of frameshift mutation improves the search performance of the standard CGP.

scholarly journals Graph representations in genetic programming

2021 ◽  
Author(s):  
Léo Françoso Dal Piccol Sotto ◽  
Paul Kaufmann ◽  
Timothy Atkinson ◽  
Roman Kalkreuth ◽  
Márcio Porto Basgalupp
Keyword(s):  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Digital Circuit ◽  
Search Performance ◽  
Genetic Operators ◽  
Code Reuse ◽  
Neutral Drift ◽  
Graph Representations ◽  
Regression Problems ◽  
The Individual

AbstractGraph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and $$(1+\lambda )$$ ( 1 + λ ) . In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the ($$1 + \lambda$$ 1 + λ ) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.

Resistance Monitor and Control for Vacuum Evaporation of Metals and Carbon

1976 ◽  
Vol 34 ◽  
pp. 572-573
Author(s):  
Russell L. Steere ◽  
Eric F. Erbe ◽  
J. Michael Moseley
Keyword(s):  
Electronic Device ◽  
Point Sources ◽  
High Contrast ◽  
Variable Resistor ◽  
Quality Of Results ◽  
Low Contrast ◽  
Evaporation Source ◽  
And Control ◽  
At Will

We have designed and built an electronic device which compares the resistance of a defined area of vacuum evaporated material with a variable resistor. When the two resistances are matched, the device automatically disconnects the primary side of the substrate transformer and stops further evaporation.This approach to controlled evaporation in conjunction with the modified guns and evaporation source permits reliably reproducible multiple Pt shadow films from a single Pt wrapped carbon point source. The reproducibility from consecutive C point sources is also reliable. Furthermore, the device we have developed permits us to select a predetermined resistance so that low contrast high-resolution shadows, heavy high contrast shadows, or any grade in between can be selected at will. The reproducibility and quality of results are demonstrated in Figures 1-4 which represent evaporations at various settings of the variable resistor.

Heat Transfer Enhancement in Separation Process of Ethanol from Ethanol Water Mixture by Using Surfactants

2020 ◽  
pp. 9-14 ◽  
Author(s):  
Acharya Anil Ramchandra ◽  
R. Kadam ◽  
A. T. Pise
Keyword(s):  
Heat Transfer ◽  
Ammonium Chloride ◽  
Sodium Dodecyl ◽  
Water Mixture ◽  
Transfer Enhancement ◽  
Surface Tension Reduction ◽  
Surface Active Agents ◽  
Surface Active ◽  
Time Required

Here the investigations are done while distillation of ethanol-water mixture for separating ethanol from fermentation process. Focus is to study reduction in time required and hence saving in energy for the distillation process of ethanol-water mixture under the influence of surface-active agents (Surfactants). This novelty is from observation of these surfactants to enhance heat transfer rate because of surface tension reduction in aqueous solutions. SDS (Sodium Dodecyl Sulphate), NH4Cl (Ammonium Chloride) and SLBS (Sodium lauryl benzene sulphonate) surfactants in different concentration are experimented. The concentration of these surfactant is varied from 1700 ppm to 2800 ppm. This range is decided by observing critical micelle concentration of used surfactants. Results showed that time is reduced and hence energy consumption is also reduced. Results shown by NH4Cl are found to be more useful as it is ecofriendly surfactant which is not affecting ethanol-water mixture. Use of ammonium chloride as surfactant in distillation is actually useful to reduce energy without hampering the quality of process is the novelty of this work.

scholarly journals CATH functional families predict functional sites in proteins

2020 ◽  
Author(s):  
Sayoni Das ◽  
Harry M Scholes ◽  
Neeladri Sen ◽  
Christine Orengo
Keyword(s):  
Functional Characterization ◽  
Functional Site ◽  
Training Data ◽  
Supplementary Information ◽  
Conserved Residues ◽  
Functional Sites ◽  
Protein Protein Interaction ◽  
Evolutionary Features ◽  
Functional Families

Abstract Motivation Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein–protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams). Results FunSite’s prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed other publicly available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite’s performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyze which structural and evolutionary features are most predictive for functional sites. Availabilityand implementation https://github.com/UCL/cath-funsite-predictor. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Public Awareness and Disaster Risk Reduction: Just-in-Time Networks and Learning

2008 ◽  
Vol 23 (3) ◽  
pp. 286-288 ◽  
Author(s):  
Ali Ardalan ◽  
Faina Linkov ◽  
Eugene Shubnikov ◽  
Ronald E. LaPorte
Keyword(s):  
Hurricane Katrina ◽  
Wide Spectrum ◽  
Public Awareness ◽  
Just In Time ◽  
Educational Levels ◽  
Hazard And Risk ◽  
Time Required ◽  
Expert Network ◽  
Core Materials

AbstractImproving public awareness through education has been recognized widely as a basis for reducing the risk of disasters. Some of the first disaster just-in-time (JIT) education modules were built within 3–6 days after the south Asia tsunami, Hurricane Katrina, and the Bam, Pakistan, and Indonesia earthquakes through a Supercourse. Web monitoring showed that visitors represented a wide spectrum of disciplines and educational levels from 120 developed and developing countries. Building disaster networks using an educational strategy seizes the opportunity of increased public interest to teach and find national and global expertise in hazard and risk information. To be effective, an expert network and a template for the delivery of JIT education must be prepared before an event occurs, focusing on developing core materials that could be customized rapidly, and then be based on the information received from a recent disaster. The recyclable process of the materials would help to improve the quality of the teaching, and decrease the time required for preparation. The core materials can be prepared for disasters resulting from events such as earthquakes, hurricanes, tsunamis, floods, and bioterrorism.

scholarly journals Monitoring and Debugging the Quality of Results in Approximate Programs

2015 ◽  
Vol 43 (1) ◽  
pp. 399-411 ◽  
Author(s):  
Michael Ringenburg ◽  
Adrian Sampson ◽  
Isaac Ackerman ◽  
Luis Ceze ◽  
Dan Grossman
Keyword(s):  
Quality Of Results

Learning to Control the Program Evolution Process with Cultural Algorithms

1997 ◽  
Vol 5 (2) ◽  
pp. 181-211 ◽  
Author(s):  
Elena Zannoni ◽  
Robert G. Reynolds
Keyword(s):  
Genetic Programming ◽  
Population Level ◽  
Extraction Process ◽  
Cultural Algorithms ◽  
Genetic Operators ◽  
Regression Problem ◽  
Standard Procedures ◽  
Programming Process ◽  
Time Required ◽  
Effective Use

Traditional software engineering dictates the use of modular and structured programming and top-down stepwise refinement techniques that reduce the amount of variability arising in the development process by establishing standard procedures to be followed while writing software. This focusing leads to reduced variability in the resulting products, due to the use of standardized constructs. Genetic programming (GP) performs heuristic search in the space of programs. Programs produced through the GP paradigm emerge as the result of simulated evolution and are built through a bottom-up process, incrementally augmenting their functionality until a satisfactory level of performance is reached. Can we automatically extract knowledge from the GP programming process that can be useful to focus the search and reduce product variability, thus leading to a more effective use of the available resources? An answer to this question is investigated with the aid of cultural algorithms. A new system, cultural algorithms with genetic programming (CAGP), is presented. The system has two levels. The first is the pool of genetic programs (population level), and the second is a knowledge repository (belief set) that is built during the GP run and is used to guide the search process. The microevolution within the population brings about potentially meaningful characteristics of the programs for the achievement of the given task, such as properties exhibited by the best performers in the population. CAGP extracts these features and represents them as the set of the current beliefs. Beliefs correspond to constraints that all the genetic operators and programs must follow. Interaction between the two levels occurs in one direction through the extraction process and, in the other, through the modulation of an individual's program parameters according to which, and how many, of the constraints it follows. CAGP is applied to solve an instance of the symbolic regression problem, in which a function of one variable needs to be discovered. The results of the experiments show an overall improvement on the average performance of CAGP over GP alone and a significant reduction of the complexity of the produced solution. Moreover, the execution time required by CAGP is comparable with the time required by GP alone.

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