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Topic: Genetic operator

	Genetic operator - Wikipedia, the free encyclopedia
		A genetic operator is a process used in genetic algorithms to maintain genetic diversity.
		Genetic variation is a necessity for the process of evolution.
		Genetic operators used in genetic algorithms are analogous to those which occur in the natural world: survival of the fittest, or selection; asexual or sexual reproduction (crossover, or recombination); and mutation.
	en.wikipedia.org /wiki/Genetic_operator (81 words)

	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, natural selection, and recombination (or crossover).
		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.
		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.
	en.wikipedia.org /wiki/Genetic_algorithm (2566 words)

	Creating Custom Genetic Operators (Site not responding. Last check: 2007-10-20)
		It may be you want to experiment around with new or different genetic operators to assess their impact upon the performance of the genetic algorithm.
		The genetic engine will run the population of Chromosomes through each of the active genetic operators in the order in which they were added to the Configuration object.
		It is the reproduction operator that guarantees that each of the chromosones in the current population become candidates for the next generation.
	jgap.sourceforge.net /docs/operators.html (873 words)

	Genetic Algorithms in Mechanism Synthesis
		Genetic algorithms, on the other hand, are simple to implement and involve evaluations of only the objective function and the use of certain genetic operators to explore the design space.
		The selected members are then operated on by one of the two genetic operators, crossover or mutation, both of which have a user-defined probability of occurrence.
		The genetic algorithm implementation uses a population size of 100 for a maximum number of 50 generations as it was found that beyond this the improvement in the objective function was negligible.
	www.ecs.umass.edu /mie/labs/Krishnamurty/papers/genetic.html (4173 words)

	Finding Optimal Solutions Using A Hybrid GA-Heuristic Search Strategy
		The hybrid approach is reflected in both the implementation of the Genetic Algorithm and in the methodology of applying it to solve problems.
		Forcing the genetic operators to produce 'legal' individuals which do not violate constraints makes the evolution process a few orders of magnitude faster than it would have been if it was relying on natural selection to satisfy constraints.
		An additional genetic operator is often needed to 'fine tune' the genetic search in the later stages of the evolution cycle.
	www.attar.com /pages/dev_gap.htm (2676 words)

	[No title]
		A genetic algorithm using this method called the vector based genetic algorithm was tested on a variety of problem functions representative of real world state spaces.
		Genetic operators are applied on pairs of chosen individuals to produce the offspring.
		The vector based genetic algorithm was allowed to optimize a number of functions, and the results obtained from the experiments were compared for performance with a genetic algorithm using crossover recombination techniques.
	www.cs.ndsu.nodak.edu /~balakris/researchPaperSCCsfin1.doc (4199 words)

	AI: Genetic Algorithms
		This is a kind of genetic selection in which the computer would use a model or procedure to test the fitness of the chromosome and assign some kind of numeric value for its fitness in comparison to other chromosomes.
		The operators in the parse tree make up what is called the function set, and can be any kind of function which is applicable for the task--i.e., they don't have to be addition/subtraction/etc, they can be actual binary functions which do some operation to both of its arguments.
		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)

	CAP-3: APPLICATIONS OF GENETIC ALGORITHMS (Site not responding. Last check: 2007-10-20)
		Dislike partial genetically defined ANNs (as (Whitley, Starkweather, Bogart, 1990) where genetic training and connectivity are addressed as separate works and (Harp, Samad, Guha, 1989) where structure is genetically defined but backpropagation is used for training) we have designed a full genetic and automatic ANN designer in order to optimize any ANN component.
		PMX is an operator proposed by Goldberg and Lingle (1985).
		CX is an operator that was proposed by Oliver et al.
	neo.lcc.uma.es /TutorialEA/semEC/cap03/cap_3.html (6040 words)

	Genetic algorithm Article, Geneticalgorithm Information (Site not responding. Last check: 2007-10-20)
		Genetic algorithms are typically implemented as a computersimulation in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to anoptimization problem evolves toward better solutions.
		Operation of a GA The problem to be solved is represented by a list of parameters which can be used to drive an evaluation procedure, called chromosomes or genomes.
		Genetic programming algorithms typically require runningtime that is orders of magnitude greater than that for genetic algorithms, but they may be suitable for problems that areintractable with genetic algorithms.
	www.anoca.org /algorithms/generation/genetic_algorithm.html (1925 words)

	index.html
		Genetic Algorithms are one type of learning algorithms patterened after the processes underlying evolution; shaping a population of individuals through the survival of its most fit members.
		Crossover: One of the genetic operator is crossover which takes the two candidate solutions and divides them, swapping components to produce two new candidates.
		The genetic algorithm continues until some termination requirement is met, such as having one or more candidate solutions whose fitness exceeds some threshold.
	www.ndsu.nodak.edu /instruct/juell/vp/cs724s00/gen_alg (517 words)

	ipedia.com: Genetic algorithm Article (Site not responding. Last check: 2007-10-20)
		Operation of a GA The problem to be solved is represented by a list of parameters which can be used to drive an evaluation procedure, called chromosomess or genomess.
		Genetic programming is a related technique developed by John Koza, in which computer programs, rather than function parameters, are optimised.
		Genetic programming algorithms typically require running time that is orders of magnitude greater than that for genetic algorithms, but they may be suitable for problems that are intractable with genetic algorithms.
	www.ipedia.com /genetic_algorithm.html (1924 words)

	Automatic molecular design using evolutionary techniques
		Genetic algorithms [Holland 75], genetic programming [Koza 92] and genetic graphs can automatically generate solutions to problems given a function that determines which of two candidate solutions is better.
		Other than the usual operators to add and remove atoms or bonds, it may be helpful to have a mutation operator that makes a random ring aromatic (alternating double and single bonds for certain sized rings).
		In the genetic programming case, a tree language to generate analog circuits compatible with the SPICE (Simulation Program with Integrated Circuit Emphasis) [Quarles 94] simulator was constructed and a 64 node (80MHz per node) parallel supercomputer was used to design the circuits.
	people.nas.nasa.gov /~globus/papers/Nanotechnology98/paper.html (6436 words)

	[No title] (Site not responding. Last check: 2007-10-20)
		A simple genetic algorithm that yields good results in many practical problems is composed of three operators: 1.
		This operator is an artificial version of natural selection, a Darwinian survival of the fittest among string creatures.
		One easy way to implement the reproduction operator is to create a biased roulette wheel where each current string in the population has a roulette wheel slot sized in proportion to its fitness.
	www.tjhsst.edu /~rlatimer/ai/WorksheetGeneticRoulette.html (455 words)

	Operator Creation Guidelines (Site not responding. Last check: 2007-10-20)
		Although the creation of operators is normally reduced to implementing one single method there are some rules to follow if a user may wish to view and use his operators in the GUI.
		For instance the mutation propability is an attribute of a mutator.
		There are three properties that are essentially important for operators in algorithms: the inputsize, the outputsize and the datatype.
	www.evolvica.org /manual/developer/guide.html (452 words)

	Vertices Wint95: Genetic Algorithms
		Whereas hill-climbing and its relatives require domain-specific information (e.g., partial derivatives) to guide their searches, the genetic algorithm requires only two things: (1) a means of representing possible solutions and (2) an objective function evaluator--a function which maps a value from the domain of possible solutions to a scalar value.
		This survival of the fittest is mirrored in the genetic algorithm through reproduction, one of the three main genetic operators.
		Careless representation schemes can also nullify the effectiveness of the crossover operator; it is possible that crossover would no longer serve to recombine useful parts of pairs of individuals, and even that crossover could create a chromosome which does not represent a legitimate solution.
	www.duke.edu /vertices/update/win95/genalg.html (2545 words)

	Introduction to Genetic Algorithms
		Genetic Algorithms were invented to mimic some of the processes observed in natural evolution.
		Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics.
		To continue the genetic analogy these individuals are likened to chromosomes and the variables are analogous to genes.
	www.doc.ic.ac.uk /~nd/surprise_96/journal/vol1/hmw/article1.html (1142 words)

	SGOPT User Manual Version Version 2.0
		Local search is another genetic operator that is sometimes employed with evolutionary algorithms to refine solutions in their local neighborhood.
		The crossover operator picks two points on the the binary representation and generates the new sample by taking all of the bits between these points from one parent and the remaining bits from the other parent.
		Geographically structured genetic algorithms (GSGAs) perform a structured selection in which individuals compete against a fixed subset of the population, and the genetic operators are applied to individuals selected from these subsets.
	www.cs.sandia.gov /~wehart/SGOPT/sgopt/doc/html/ea.html (1155 words)

	Computer Science Project Proposal
		Devising this encoding, as well as implementing the necessary genetic operators so that they properly modify and improve upon our initial population of solutions is the fundamental focus of this project.
		The mutation operator is the easiest to implement, and consists of performing one of three possible alterations.
		Similarly, fitness-based mate selection is a chromosome-level operation when it applies the fitness function, but it is a population-level operation at the point of moving the fittest chromosomes from the normal population into the mating pool.
	www.earlham.edu /~chrisma/proposal.htm (3273 words)

	[No title]
		Introduction This paper presents a new crossover operator for Genetic Algorithm, the SENSE operator.
		GENEtic Search Implementation System 4.5 (GENESIS 4.5) was developed by John Grefenstette to promote the study of genetic algorithm for function minimization.
		This operator performs the crossover in problem space rather than the chromosome space and tends to address some of the issues faced in traditional crossover.
	www.ndsu.nodak.edu /instruct/juell/vp/asc03/385-030.doc (1783 words)

	Implementation Details
		Parents are selected for processing by a genetic operator so as to give more chances of reproduction to members of the current population that have the largest fitness values, i.e., to members that represent a better solution to the problem under investigation.
		To illustrate the effects of the genetic operators, assume that a binary chromosome is being used, in which the I-th bit in each chromosome describes the presence (one) or absence (zero) in a molecule of the I-th substructural feature in a dictionary of such features.
		There are many different types of crossover operator, but they all involve selecting two parent chromosomes by roulette-wheel selection (as described above), taking some subset of one parent chromosome and the complementary subset from the other parent, and then combining the two subsets in some way to yield new, child chromosomes.
	www.dur.ac.uk /j.m.hutson/ccp6-98/node33.html (704 words)

	[No title] (Site not responding. Last check: 2007-10-20)
		Three major approaches to using genetic algorithms for machine learning are described, and an example of their use in learning entire task programs is given.
		Genetic algorithms can be used for both numerical parameters optimization and combinatorial search.
		This paper focuses on invariants across these genetic algorithms that differ along two dimensions: (1) the way user-defined objective function is mapped to a fitness measure, and (2) the way the fitness measure is used to assign offspring to parents.
	www.aic.nrl.navy.mil /galist/digests/v5n41 (1774 words)

	Technical Reports (Site not responding. Last check: 2007-10-20)
		Although a subclass of the class of genetic algorithms, this algorithm and its variations represent a unique approach with many interesting results.
		The primary distinguishing feature is that when a pair of offspring are created and chosen as worthy of membership in the population they replace their parents.
		With no mutation this has the effect of maintaining the original genetic material over time, although it is reorganized.
	www.cs.ualberta.ca /~cdjones/TR/1992/TR92-02.php (279 words)

	Genetic Algorithm Software - GA Software
		Crossover is a genetic operator that combines (mates) two chromosomes (parents) to produce a new chromosome (offspring).
		A crossover operator that randomly selects a crossover point within a chromosome then interchanges the two parent chromosomes at this point to produce two new offspring.
		A crossover operator that decides (with some probability – know as the mixing ratio) which parent will contribute each of the gene values in the offspring chromosomes.
	www.nd.com /genetic/crossover.html (628 words)

	Genetic Algorithms: Mutation
		Mutation is a genetic operator that alters one ore more gene values in a chromosome from its initial state.
		Mutation is an important part of the genetic search as help helps to prevent the population from stagnating at any local optima.
		This mutation operator keeps the population from stagnating in the early stages of the evolution then allows the genetic algorithm to fine tune the solution in the later stages of evolution.
	www.nd.com /products/genetic/mutation.htm (363 words)

	genetic mutation (Site not responding. Last check: 2007-10-20)
		Mutation is a genetic operator that alters one ore more gene values in a chromosome from...
		Three teams of scientists have identified a genetic mutation that is linked to about 5 percent of inherited cases of Parkinson's disease.
		The finding opens the door to the possibility of genetic screening for the LRRK2 gene mutation, which is believed to be the most common genetic cause of inherited Parkinson's disease identified to date.
	www.dnatestingsources.com /special/genetic-mutation (680 words)

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