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Topic: Chromosome (genetic algorithm)

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Genetic operator

Fitness function

Crossover (genetic algorithm)

Heuristic

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	Genetic Algorithm...Artilifes.com
		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.
		Chromosomes are typically represented as simple strings of data and instructions, in a manner not unlike instructions for a von Neumann machine, although a wide variety of other data structures for storing chromosomes have also been tested, with varying degrees of success in different problem domains.
		As a general rule of thumb genetic algorithms might be useful in problem domains that have a complex fitness landscape as recombination is designed to move the population away from local minima that a traditional hill climbing algorithm might get stuck in.
	www.artilifes.com /geneticalgorithms.html (1458 words)

	Genetic Algorithm
		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].
		Crossover is one of the genetic operators used to recombine the population genetic material.
		Critical to the algorithm performance is the choice of underlying encoding for the solution of the optimization problem (the individuals on the population).
	java.icmc.sc.usp.br /dilvan/thesis.phd/genetic.html (1981 words)

	Genetic Algorithm (Site not responding. Last check: 2007-10-12)
		Mutation (genetic algorithm) - In genetic algorithms, mutation is a genetic operator used to maintain genetic diversity from one generation of a population of chromosomes to the next.
		Chromosome (genetic algorithm) - In genetic algorithms, a chromosome (also sometimes called a genome) is a set of parameters which define a proposed solution to the problem that the genetic algorithm is trying to solve.
		Genetic algorithms mimic the natural process of evolution, helping engineers optimize their designs by using the principle of "survival of the fittest." VLSI is especially suited to benefit from genetic algorithms - genetic algorithm and this comprehensive book shows you how to get the best results, fast.
	www.lepcoinc.com /geneticalgorithm.html (999 words)

	Generation 5: Artificial Intelligence Repository - Introduction to Genetic Algorithms
		Genetic algorithms (or GA) were created to combat these problems.
		Genetic algorithms are not too hard to program or understand, since they are biological based.
		Al Biles uses genetic algorithms to filter out 'good' and 'bad' riffs for jazz improvisation, the military uses GAs to evolve equations to differentiate between different radar returns, stock companies use GA-powered programs to predict the stock market.
	library.thinkquest.org /18242/ga.shtml (1096 words)

	AI: Genetic Algorithms
		Genetic algorithms provide computers with a method of problem-solving which is based upon implementations of evolutionary processes.
		In this case, the chromosomes have a variable length (as opposed to data structures which usually have a fixed length), and the genomes are represented as simple data structures which symbolize sub-trees of computer code and are arranged in a heirarchial order called a parse tree.
		If genetic algorithms use the array of configurable gates in an FPGA as their digital chromosome, then they can be used to "genetically evolve" the chip for a particular task.
	biology.kenyon.edu /slonc/bio3/AI/GEN_ALGO/gen_algo.html (1896 words)

	Genetic Algorithm Software - GA Software
		Genetic algorithms are general-purpose search algorithms based upon the principles of evolution observed in nature.
		Genetic algorithms search for this optimal solution until a specified termination criterion is met.
		A genetic algorithm creates an initial population (a collection of chromosomes), evaluates this population, then evolves the population through multiple generations (using the genetic operators discussed above) in the search for a good solution for the problem at hand.
	www.nd.com /genetic/whatisga.html (218 words)

	Crossover (genetic algorithm) - Wikipedia, the free encyclopedia
		In genetic algorithms, crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next.
		It is an analogy to reproduction and biological crossover, upon which genetic algorithms are based.
		One such case is when the chromosome is an ordered list, such as an ordered list the cities to be travelled for the traveling salesman problem.
	en.wikipedia.org /wiki/Crossover_(genetic_algorithm) (519 words)

	Genetic Algorithm - GPWiki
		The basic genetic algorithm attempts to evolve traits that are optimal for a given problem.
		This is the main part of the genetic algorithm, where the strong survive and the weak perish.
		Now that you are familiar with the general idea of a genetic algorithm, this summary of the procedure should cement the concept nicely in your mind.
	gpwiki.org /index.php/Genetic_Algorithm (1174 words)

	Genetic Algorithms Overview
		Genetic algorithms are one of the best ways to solve a problem for which little is known.
		Genetic algorithms use the principles of selection and evolution to produce several solutions to a given problem.
		Genetic algorithms tend to thrive in an environment in which there is a very large set of candidate solutions and in which the search space is uneven and has many hills and valleys.
	geneticalgorithms.ai-depot.com /Tutorial/Overview.html (1459 words)

	Genetic Algorithm
		Genetic algorithms [1, 2, 5] have become a viable solution to strategically perform a global search by means of many local searches.
		The basis of the genetic algorithm methods is derived from the mechanisms of evolution and natural genetics.
		A genetic algorithm works by building a population of chromosomes which is a set of possible solutions to the optimization problem.
	www.ll.mit.edu /HPECchallenge/ga.html (1216 words)

	Chromosome - Article from FactBug.org - the fast Wikipedia mirror site (Site not responding. Last check: 2007-10-12)
		Chromosomes were first observed by Karl Wilhelm von Nägeli in 1842 and their behavior later described in detail by Walther Flemming in 1882.
		Sexually reproducing species have somatic cells (body cells), which are diploid [2n] (they have two sets of chromosomes, one from the mother, one from the father) or polyploid [Xn] (more than two sets of chromosomes), and gametes (reproductive cells) which are haploid [n] (they have only one set of chromosomes).
		Some chromosome abnormalities do not cause disease in carriers, such as translocations, or chromosomal inversions, although it may lead to a higher chance of having a child with an chromosome disorder.
	www.factbug.org /cgi-bin/a.cgi?a=6438 (1073 words)

	genome.gov \| 1995 Release:, Map of Human Chromosome 22
		Constructed in overlapping, cloned segments of the chromosome, called YAC contigs, a physical map offers greater resolution than the initial genetic map, which yields general marker order, but much less resolution than the actual sequencing of the chromosome.
		Chromosome 22 is the third smallest human chromosome, spanning an estimated 50 million base pairs.
		Although chromosome 22 represents a small piece of the human genome, like Pennsylvania to the entire United States, the researchers found that traversing its complex, molecular terrain was no Sunday stroll through Independence Square.
	www.genome.gov /10000524 (880 words)

	Bruce L Jacob: Composing with Genetic Algorithms
		Presented is an application of genetic algorithms to the problem of composing music, in which GAs are used to produce a set of data filters that identify acceptable material from the output of a stochastic music generator.
		The solutions are represented by chromosomes, strings of alleles represented by strings of numbers, and the recombination of chromosomes is simply a matter of creating new strings with alleles taken from the parent chromosomes.
		The module is a collection of chromosomes, each of which acts as a data filter that identifies harmonic combinations as "good" or "bad." Before composition begins, the chromosomes are evolved to reflect the musical tastes of the human operator.
	www.ee.umd.edu /~blj/algorithmic_composition/icmc.95.html (1878 words)

	Karnig.co.uk \| General Genetic Algorithm Tool \| User manual (Site not responding. Last check: 2007-10-12)
		The difference between the best and worst genotype’s fitness is recorded at the start of a genetic algorithm execution and when this difference becomes smaller than the percentage selected by this convergence factor of the initial difference, the genetic algorithm terminates.
		After an genetic algorithm terminates (or it is stopped by the user) a summary of the best and median’s fitness of the population, and the phenotype of the fittest genotype found (possibly a solution to the problem).
		When a new set of genetic algorithms are executed and logged, they will replace old log files, therefore the user should use and save a copy of the log files used before a new set of genetic algorithms is executed, within a new Execution window.
	www.karnig.co.uk /ga/manual.html (2641 words)

	Genetic Algorithms, Evolving Systems and Optimization (Site not responding. Last check: 2007-10-12)
		We have considered critically the standard building block hypothesis in the context of "standard" genetic algorithms and in the case of a Kauffman Nk model.
		NNCP is also using genetic algorithms in the development of price prediction algorithms for use in financial markets (see Applications).
		In this case the chromosome is viewed as an algorithm and the phenotype as the result of the computation.
	www.nuclecu.unam.mx /~nncp/genetic.html (1461 words)

	Genetic Algorithm Graphing Applet (Site not responding. Last check: 2007-10-12)
		The program accepts parameters for the number of generations (default 100), the size of the population (default 100), the size of the chromosomes (default 20), the probability of crossover for every two chromosomes selected for reproduction (default 0.7), and the probability for a bit to randomly mutate (default 0.001).
		The red line represents the fitness of the current best chromosome in the population, the blue line represents the average fitness of the population, and the green line shows the fitness of the worst chromosome in the population (which can be an interesting thing to observe).
		This is because as the population improves, it becomes nearly impossible for chromosomes with poor fitness to advance to the next generation (we also observe that with tournament selection, there is no way for the (unique) worst chromosome in the population to advance).
	www.cs.hmc.edu /~phenry/final/GeneticApplet.html (930 words)

	Genetic algorithm Summary
		Genetic algorithms (GAs) are artificial procedures seek to achieve similar success in a wide variety of optimization problems--design problems where the best possible solution is sought--by mimicking the principles behind biological evolution.
		A typical genetic algorithm requires two things to be defined: (1) a genetic representation of solutions, (2) a fitness function to evaluate them.
		Genetic algorithms originated from the studies of cellular automata, conducted by John Holland and his colleagues at the University of Michigan.
	www.bookrags.com /Genetic_algorithm (4145 words)

	Linux: Tuning The Kernel With A Genetic Algorithm \| KernelTrap
		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.
		And as i said in a previous comment: using a genetic algorithm for tweaking schedulers is conceptually flawed, because the nature of the problem they both need to solve is exactly the same.
		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 (4853 words)

	Genetic Algorithm
		Genetic Algorithms take the essential aspects of this picture of biological evolution and represent them as computer models.
		The chromosome determining the strategy inherited by an offspring was constructed from the chromosomes of its parents using two `genetic operators': crossover and mutation.
		The effect is that the offspring inherits part of the chromosome from one parent and part from the other.
	home.versatel.nl /marco_huigen/genetic_algorithm.htm (840 words)

	Hybrid Genetic Algorithm
		This is one of the drawbacks of using a genetic algorithm for optimization - since there is no guarantee of optimality, there is always the chance that there is a better chromosome lurking somewhere in the search space.
		So, if we use a hybrid algorithm, the problem reduces to ensuring that we run the GA as many times as is needed to pick out all the good regions.
		This has the effect of moving the top chromosomes in that generation (which are the result of exponential convergence toward the best regions) to the local maximum in their region.
	www.cimms.ou.edu /~lakshman/Papers/ga/node8.html (377 words)

	Genetic Algorithms - Nature's Way
		Yes, this is the general field known as Evolutionary Computation, of which the Genetic Algorithm is the basic technique and can be further expanded to remove the need for an underlying program (in the field of Genetic Programming).
		Well, we create a population of individuals, each represented by a chromosome (a collection of genes or characteristics) appropriate to the problem we are trying to investigate.
		In the Genetic Programming extension, the genes are the valid statements within a computer language and the chromosome in this case is itself the program.
	www.calresco.org /genetic.htm (1285 words)

	FAQ: comp.ai.genetic part 2/6 (A Guide to Frequently Asked Questions) - Q1.1: What's a Genetic Algorithm (GA)?
		The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature.
		The genetic algorithm uses stochastic processes, but the result is distinctly non-random (better than random).
		GENETIC ALGORITHMs are used for a number of different application areas.
	www-2.cs.cmu.edu /Groups/AI/html/faqs/ai/genetic/part2/faq-doc-2.html (804 words)

	Genetic Algorithm
		The first step in genetic algorithm is to “translate” the real problem into “biological terms”.
		In permutation encoding, every chromosome is a string of numbers, which represents number in a sequence.
		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)

	Genetic Algorithms Demo
		The genetic algorithm is described fully by John Holland in his book, "Adaptation in Natural and Artificial Systems." More popular accounts can be found in the books "Complexity" by M. Mitchell Waldrop and "Artificial Life" by Steven Levy.
		The genetic algorithm can be applied to many different types of problems, but GA uses it to evolve simulated "organisms" called Eaters in a simulated world that contains simulated plants for the Eaters to eat.
		Crossover here means that the chromosomes exchange some genetic material; a random position between 1 and 128 is chosen and all data on the chromosomes after that position are swapped between the two chromosomes.
	math.hws.edu /xJava/GA (1495 words)

	Indirectly-mapped, Multi-chromosome Genetic Algorithm
		Presently, evolvable hardware is a nascent field troubled by difficulty in “brittleness” and “fragility” of the systems, the difficulty of evolving, and the difficulty of scaling from small problems to large ones.
		The Johns Hopkins University Applied Physics Laboratory has invented and is patenting a mathematical algorithm to mimic the process of evolutionary biology.
		The algorithm features an indirect mapping between the part of the system that is evolved (genotype) and the physical instantiation of the system (phenotype).
	www.jhuapl.edu /ott/technologies/technology/Articles/P02074.asp (120 words)

	Chromosome (genetic algorithm) - Wikipedia, the free encyclopedia
		In genetic algorithms, a chromosome (also sometimes called a genome) is a set of parameters which define a proposed solution to the problem that the genetic algorithm is trying to solve.
		The chromosome is often represented as a simple string, although a wide variety of other data structures are also used.
		The design of the chromosome and its parameters is by necessity specific to the problem to be solved.
	en.wikipedia.org /wiki/Chromosome_(genetic_algorithm) (324 words)

	The Genetic Algorithm
		The genetic code that determines the fitness of an individual is termed, logically enough, the chromosome of that individual.
		In our case, this is done by performing the BWER detection analysis on all the truthed cases in the verification database using the chromosome and finding the skill score of the resulting detections.
		Koza (1992) for example), it is overkill in weather detection algorithms where we already know the form of the solution or, at least, the shape of the membership functions of all the fuzzy sets.
	www.cimms.ou.edu /~lakshman/Papers/ga/node3.html (204 words)

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