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Topic: Selection (genetic algorithm)

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Roulette wheel selection

Genetic algorithm

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	Genetic algorithm - Wikipedia, the free encyclopedia
		Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).
		Genetic algorithms are typically implemented as a computer simulation in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to an optimization problem evolves toward better solutions.
		Genetic algorithms originated from the studies of cellular automata, conducted by John Holland and his colleagues at the University of Michigan.
	en.wikipedia.org /wiki/Genetic_algorithm (2911 words)

	Category:Genetic algorithms - Wikipedia, the free encyclopedia
		A genetic algorithm (GA) is an algorithm used to find approximate solutions to difficult-to-solve problems through application of the principles of evolutionary biology to computer science.
		Genetic algorithms use biologically-derived techniques such as inheritance, mutation, natural selection, and recombination.
		Genetic algorithms are a particular class of evolutionary algorithms.
	en.wikipedia.org /wiki/Category:Genetic_algorithms (99 words)

	UAMIS AI Group - Genetic Algorithm Optimizer (Site not responding. Last check: 2007-09-10)
		Initially, when the genetic algorithm program is started for the first time or reset, all the agents in the population are given random values for their genes which results in random X values being represented since the genes determine the X position.
		The probablility for selection is based on the agent's Y value relative to the rest of the population (survival of the fittest).
		Selection begins by determining an agents relative fitness by dividing its Y value by the sum of all the Y values for agents in the population.
	ai.bpa.arizona.edu /~mramsey/ga.html (1118 words)

	Genetic Algorithms - John H. Holland
		Selection is simple: if an organism fails some test of fitness, such as recognizing a predator and fleeing, it dies.
		This remarkable ability of genetic algorithms to focus their attention on the most promising parts of a solution space is a direct outcome of their ability to combine strings containing partial solutions.
		Two strings line up, a point along the strings is selected at random and the portions to the left of that point are exchanged to produce two offspring: one containing the symbols of the first string up to the crossover point and those of the second beyond it, and the other containing the complementary cross.
	www.econ.iastate.edu /tesfatsi/holland.GAIntro.htm (4694 words)

	Genetic Algorithm
		Specifically evolution's driving force is the combination of natural selection and the change and recombination of genetic material that occurs during reproduction [17].
		Genetic algorithms, first proposed by Holland in 1975 [64], are a class of computational models that mimic natural evolution to solve problems in a wide variety of domains [65].
		Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies.
	java.icmc.sc.usp.br /dilvan/thesis.phd/genetic.html (1981 words)

	Genetic Algorithms and Design (Site not responding. Last check: 2007-09-10)
		Holland's schema theorem is fundamental to the theory of genetic algorithms.
		This means that genetic algorithms exploit syntactic similarities in the genotype as long as the building blocks (short, high-performance schemas) lead to near-optima.
		The genetic algorithm is also quite resistant to noise in the evaluation process and noisy judgments are processed in useful ways [DeJong, 1975].
	www.cse.unr.edu /~sushil/papers/thesis/thesishtml/node2.html (3030 words)

	Genetic Algorithms (Site not responding. Last check: 2007-09-10)
		The genetic algorithms' strength come from the implicitly parrallel search of the solution space that it performs via a population of candidate solutions and this population is manipulated in the simulation.
		Genetic Algorithms are adaptive to their environments, and as such this type of method is appealing to the vision community who must often work in a changing environment.
		Genetic algorithms are currently the most prominent and widely used computational models of evolution in artificial-life systems.
	www-dse.doc.ic.ac.uk /~nd/surprise_96/journal/vol4/tcw2/report.html (4712 words)

	A Genetic Algorithm for Empirical Variable Selection
		A genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction.
		A genetic algorithm can be used as a heuristic for function optimization where the extrema of the function (i.e., minima or maxima) cannot be established analytically.
		Select parents from the current population to undergo genetic operations to form offspring.
	dsg.harvard.edu /~svinterb/dist/lrga.html (734 words)

	GA (Site not responding. Last check: 2007-09-10)
		Genetic Algorithm vs. Conventional AI Most symbolic AI systems are very static and usually only solve one given specific problem, since their architecture is designed for whatever that specific problem was in the first place.
		In Genetic Algorithms, a large number of individual solution sets are created and are evaluated for their fitness.
		Chromosomes are selected from the population to be parents to crossover.
	www.learncool.com /Nikhil/AI/GA.htm (969 words)

	[No title]
		GP is one instance of the class of techniques called evolutionary algorithms, which are based on insights from the study of natural selection and evolution.
		When the tenth generation is completed, select the generation summary from Generation 1, and all of the ants in that generation appear in the listbox beneath it.
		The probability of selecting a particular parent is the ratio of that parent's fitness, divided by the total fitness of all parents.
	msdn.microsoft.com /msdnmag/issues/04/08/GeneticAlgorithms/default.aspx (5662 words)

	Improving Shellsort Through Evolution
		The goal of this application of a genetic algorithm is to evolve a good sequence of increments for Shellsort.
		The important parts of any application of a genetic algorithm are representation of the organisms being optimized, the method of comparing them (fitness), the method of constructing an organism from the bit-strings manipulated by the genetic algorithm, and the methods of selection, mating, and mutation.
		For this application of a genetic algorithm, an organism is a sequence of increments.
	www.cybertiggyr.com /gene/htdocs/shiva-0/shiva-0.html (2253 words)

	Linux: Tuning The Kernel With A Genetic Algorithm
		Genetic algorithms as used in machine learning are modeled after the process of evolution as observed in nature, and are a field within the science of artificial intelligence.
		Natural selection figures out how to "hack" the settings to figure out the optimal performance, but i wouldn't trust these performances in the long run in the dynamic environment your system resides in, as it'll turn out to be a horrible choice of parameters for the slightest change in workload.
		Throughput, latency and fairness maximation for any workload is the goal, and whether the selection mechanism in the genetic algorithm solves that by tuning the settings of a dumb scheduler or the scheduler fulfills its purpose only moves the place where the decisions have to be made.
	kerneltrap.org /node/4493 (5276 words)

	Human Based Genetic Algorithm
		Genetic algorithms that use human judgment to evaluate solutions are known as interactive genetic algorithms.
		Genetic algorithms were successfully applied to many diverse tasks and proved to be a nice combination of generality and efficiency for global search, optimization, and problem solving.
		To maintain diversity, selection operator applied rarely until the population will acquire the necessary size, corresponding to the importance of the problem for the community (the expressed interest of people counted as the sum of their ratings divided by the number of problem person has in his interest list, see section on motivation system).
	www.geocities.com /alex+kosorukoff/hbga/hbga.html (13805 words)

	What is a Genetic or Evolutionary Algorithm?
		In a "genetic algorithm," the problem is encoded in a series of bit strings that are manipulated by the algorithm; in an "evolutionary algorithm," the decision variables and problem functions are used directly.
		Fifth -- inspired by the role of natural selection in evolution -- an evolutionary algorithm performs a selection process in which the "most fit" members of the population survive, and the "least fit" members are eliminated.
		A drawback of any evolutionary algorithm is that a solution is "better" only in comparison to other, presently known solutions; such an algorithm actually has no concept of an "optimal solution," or any way to test whether a solution is optimal.
	www.solver.com /gabasics.htm (701 words)

	Feature selection using Genetic Algorithm
		However, in our case, sometimes selecting or removing a feature from a set does not improve or reduce the error rate, and so the algorithm will not be able to tend to the best set of features, but to different set of features that can be combined to reveal what are the significant features.
		The crossover operation consists in selecting pairs in the population, and to mix the genes of the chromosomes of a pair.
		20 new chromosomes are thus selected among the offspring of the previous population.
	silkswift.ifrance.com /ga (1340 words)

	Genetic Algorithm
		"[G]enetic algorithms are based on a biological metaphor: They view learning as a competition among a population of evolving candidate problem solutions.
		Evolutionary algorithms, also known as genetic algorithms or GAs, take their cue from biological evolution, which can turn a crawling reptile into a soaring bird without any kind of forward-looking blueprint.
		"Genetic algorithms and their relations, which fall under the umbrella term evolutionary computing, are being harnessed to optimize designs of all sorts.
	www.aaai.org /AITopics/html/genalg.html (3296 words)

	Genetic Algorithm
		Solutions, which are selected to construct new solutions (offspring), are selected according to their fitness - the fitter they are, the more chances they can have for reproduction.
		The first step in genetic algorithm is to “translate” the real problem into “biological terms”.
		In short, a genetic algorithm is a stochastic process that exhibits variable performances.
	www.bridgeport.edu /sed/projects/449/Fall_2000/fangmin/chapter4.htm (1041 words)

	ECS EPrints Service - Modelling the Dynamics of a Steady State Genetic Algorithm (Site not responding. Last check: 2007-09-10)
		A comparison is made between the dynamics of steady state and generational genetic algorithms using the statistical mechanics approach developed by Prugel-Bennett, Shapiro and Rattray.
		It is shown that the loss of variance of the population under steady state selection - genetic drift - occurs at twice the rate of generational selection.
		By considering a simple ones counting problem with selection and mutation, it is shown that, with weak selection, the steady state genetic algorithm can reproduce the dynamics of the generational genetic algorithm at half the computational cost in terms of function evaluations.
	eprints.ecs.soton.ac.uk /451 (206 words)

	Excursus: Roulette wheel selection
		We use a normal genetic algorithm with the characteristics shown in table 3.5.
		While the performance of the genetic algorithm is relative immune against changes in the parameters in table 3.5, it is highly sensible to even little changes in the fitness function.
		In the ``sterile'' environment of a normal genetic algorithm, the problem is best tackled from the other side, by guaranteeing the reproduction of the best individual.
	www.uni-koblenz.de /~gb/projects/ale/studienarbeit_html/node19.html (637 words)

	ECS EPrints Service - Genetic Drift in Genetic Algorithm Selection Schemes (Site not responding. Last check: 2007-09-10)
		A method for calculating genetic drift in terms of changing population fitness variance is presented.
		The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection, steady-state selection with varying generation gap, a simple model of Eshelman's CHC algorithm, and evolution strategies.
		The effects of changing genetic drift on the convergence of a GA are demonstrated empirically.
	eprints.ecs.soton.ac.uk /688 (159 words)

	Genetic algorithm (Site not responding. Last check: 2007-09-10)
		As you will see genetic algorithms provide several possibilities how this could be done.
		Genetic algorithms try to simulate the evolution found in nature.
		Genetic algorithms were first introduced in the lates 60ies and the first publication about this topic was released 1975 by J. Holland in his book "Adaption in Natural and Artificial Systems".
	www.cybrain.org /doc/c624.html (476 words)

	SPACE.com -- Darwinian Selection of Satellite Orbits for Military Use
		The genetic algorithm selected the best-performing constellations by interchanging variables such as how far apart the satellites are from each other, the heading of the satellites as they cross the equator, and how high they are above the Earth's surface.
		As with evolution, genetic algorithms are known for producing unexpected results.
		But in the best-performing configurations discovered by the genetic algorithm, the satellites were not spaced at equal distances.
	www.space.com /news/darwin_satellites_011016.html (597 words)

	Automatic beam angle selection in IMRT planning using genetic algorithm (Site not responding. Last check: 2007-09-10)
		The selection of suitable beam angles in external beam radiotherapy is at present generally based upon the experience of the human planner.
		The requirement to automatically select beam angles is particularly highlighted in intensity-modulated radiation therapy (IMRT), in which a smaller number of modulated beams is hoped to be used, in comparison with conformal radiotherapy.
		A genetic algorithm (GA) incorporated with an immunity operation is used to select suitable beam angles, and a conjugate gradient (CG) method is used to quickly optimize intensity maps for each selected beam combination based on a dose-based objective function.
	stacks.iop.org /0031-9155/49/1915 (459 words)

	Selection of Optimal Construction Robot Using Genetic Algorithm (Site not responding. Last check: 2007-09-10)
		The problem of optimizing the robot’s parameters using GA as a tool is analyzed on the model of an automated device for use in the restoration of steel bridges.
		The robot’s key parameters are considered simultaneously, and optimized, with respect to selected criteria.
		A detailed example illustrating the methodology is presented, and comments are made on the expected effectiveness of the method and the quality of the expected outcome.
	www.pubs.asce.org /WWWdisplay.cgi?9702676 (140 words)

	Selection of Attributes for Modeling Bach Chorales by a Genetic Algorithm (Site not responding. Last check: 2007-09-10)
		A genetic algorithm selected combinations of attributes for a machine learning system.
		The algorithm used 90 Bach chorale melodies to train models and randomly selected sets of 10 chorales for evaluation.
		Citation: Mark A. Hall, "Selection of Attributes for Modeling Bach Chorales by a Genetic Algorithm," annes, p.
	csdl.computer.org /comp/proceedings/annes/1995/7174/00/71740182abs.htm (174 words)

	Simultaneous Feature Extraction and Selection Using a Masking Genetic Algorithm - Raymer, Punch, Goodman, Sanschagrin, ... (Site not responding. Last check: 2007-09-10)
		Our previous research has shown that a hybrid between a k-nearest-neighbors (knn) classifier and a genetic algorithm (GA) can reduce the size of the feature set used by a classifier, while simultaneously weighting the remaining features to allow greater...
		0.2: Hybridised Genetic Algorithm and K-Nearest Neighbour for Rainfall..
		16 Hybridizing the Genetic Algorithm and the K Nearest Neighbor..
	citeseer.ifi.unizh.ch /9444.html (443 words)

	GAUL: Genetic Algorithm Utility Library
		The Genetic Algorithm Utility Library (or, GAUL for short) is a flexible programming library designed to aid in the development of applications that use genetic, or evolutionary, algorithms.
		Additional stochastic algorithms are provided for comparison to the genetic algorithms.
		A selection of common chromosome types are built-in.
	gaul.sourceforge.net (627 words)

	FORTRAN Genetic Algorithm
		Very briefly, a genetic algorithm is a search/optimization technique based on natural selection.
		The genetic algorithm is a computer simulation of such evolution where the user provides the environment (function) in which the population must evolve.
		This genetic algorithm (GA) driver is free for public use.
	cuaerospace.com /carroll/ga.html (709 words)

	University of Trento - Italy - UNITN-Eprints - QGA: a Quantum Genetic Algorithm
		Malossini, Andrea and Blanzieri, Enrico and Calarco, Tommaso (2004) QGA: a Quantum Genetic Algorithm.
		The complexity of the selection procedure of a genetic algorithm that requires reordering, if we restrict the class of the possible fitness functions to non–local or time–dependent fitness functions, is O(N logN) where N is the size of the population.
		In QGA the classical fitness evaluation and selection procedures are replaced by a single quantum procedure.
	eprints.biblio.unitn.it /archive/00000711 (184 words)

	Feature Subset Selection Using A Genetic Algorithm - Yang, Honavar (ResearchIndex) (Site not responding. Last check: 2007-09-10)
		Abstract: : Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified.
		This is due to the fact that the performance of the classifier (usually induced by some learning algorithm) and the cost of classification are sensitive to the choice of the features used to construct the classifier.
		11 Using genetic algorithms to select inputs for neural network..
	sherry.ifi.unizh.ch /31745.html (1021 words)

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