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Topic: Crossover (genetic algorithm)

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Genetic operator

Chromosome (genetic algorithm)

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	Genetic Algorithms - John H. Holland
		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.
		The genetic algorithm exploits the higher-payoff, or "target," regions of the solution space, because successive generations of reproduction and crossover produce increasing numbers of strings in those regions.
		Implicit parallelism also helps genetic algorithms to cope with nonlinear problems - those in which the fitness of a string containing two particular building blocks may be much greater (or much smaller) than the sum of the fitnesses attributable to each building block alone.
	www.econ.iastate.edu /tesfatsi/holland.GAIntro.htm (4694 words)

	Genetic Algorithm
		Crossover (genetic algorithm) - In genetic algorithms, crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next.
		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.
		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)

	Genetic algorithm Summary
		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.
		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 optima that a traditional hill climbing algorithm might get stuck in.
	www.bookrags.com /Genetic_algorithm (4145 words)

	Introduction to Genetic Algorithms
		Genetic algorithms are used in search and optimization, such as finding the maximum of a function over some domain space.
		Pairs of chromosomes in the new population are chosen at random to exchange genetic material, their bits, in a mating operation called crossover.
		Only one crossover occurs in the first generation: chromosomes 2 and 3 exchange their genes to the left of bit position 3.
	www.mcs.drexel.edu /~shartley/geneticAlgorithms.html (1298 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].
		Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies.
		Crossover is one of the genetic operators used to recombine the population genetic material.
	java.icmc.sc.usp.br /dilvan/thesis.phd/genetic.html (1981 words)

	Genetic Algorithm/Neural Net
		Genetic Algorithm is one way of machine learning that is analogous to evolution in living organisms.
		Genetic Algorithm is certainly a fancy notion, with the inspiration from one important theory of Nature.
		Similar to genetic algorithm, this is another algorithm that is inspired by the theory of a different field.
	web.mit.edu /chungc/6.034/l9.html (1986 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, Evolving Systems and Optimization (Site not responding. Last check: 2007-10-14)
		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).
		For $k=0$, with a random initial population, in the first step of evolution the contribution from schemata reconstruction is equal to that of schemata destruction leading to a scale invariant situation where the contribution to fitness of schematas of size $l$ is independent of $l$.
	www.nuclecu.unam.mx /~nncp/genetic.html (1461 words)

	Fine-grained Parallel Genetic Algorithms in Charm++
		Genetic algorithms [2,4] are a robust optimization tool that can be used to solve a wide range of difficult problems efficiently and accurately.
		Fine-grained genetic algorithms divide the population into small subpopulations containing only one or a couple of solutions which are connected in a grid topology (usually, 2D grids are used).
		In our implementation of the fine-grained genetic algorithm, the population of the candidate solutions is mapped to a 2D grid where each position of the grid may either contain a particular solution or be empty.
	www.acm.org /crossroads/xrds8-3/fineGrained.html (3854 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)

	Genetic Algorithm - DmWiki
		Genetic algorithms are a way of solving problems based on an evolving "survival-of-the-fittest" approach, inspired by Darwin's theory of evolution.
		The algorithm begins with a set of solutions called a population.
		Crossover is the term used to describe how the chromosomes from each parent contribute to the offspring.
	www.devmaster.net /wiki/Genetic_Algorithm (213 words)

	Implementing a Genetic Algorithm in C# and .NET (Site not responding. Last check: 2007-10-14)
		Crossover the Genome pairs in the allowable population.
		The hardest part of executing a genetic algorithm is coming up with a fitness function once the appropriate classes are in place.
		Genetic algorithms are a great way to solve problems through a trial an error process that progresses very quickly.
	www.c-sharpcorner.com /Code/2002/July/GeneticAlgorithm.asp (1173 words)

	Intro to Genetic Algorithms (Site not responding. Last check: 2007-10-14)
		Genetic algorithms were formally introduced in the United States in the 1970s by John Holland at University of Michigan.
		The genetic algorithm then creates a population of solutions and applies genetic operators such as mutation and crossover to evolve the solutions in order to find the best one(s).
		The three most important aspects of using genetic algorithms are: (1) definition of the objective function, (2) definition and implementation of the genetic representation, and (3) definition and implementation of the genetic operators.
	lancet.mit.edu /~mbwall/presentations/IntroToGAs (200 words)

	Studio5670Wiki - Genetic Algorithms
		A Genetic Algorithm is an algorithm used to find approximate solutions to difficult-to-solve problems through application of the principles of evolutionary biology.
		Genetic algorithms are typically implemented as a computer simulation in which an optimization problem evolves toward better solutions.
		Multiple individuals are stochastically (judgment based on inconclusive or incomplete evidence; guesswork) selected from the current population (based on their fitness), modified (mutated or recombined) to form a new population.
	www.uta.edu /architecture/wiki/studio5670/index.php/GeneticAlgorithms (154 words)

	Segmentation using genetic algorithm
		Genetic algorithm is used in the new two-pass optimization process.
		To apply genetic algorithm under the multiresolution framework, a new encoding mechanism for the quadtree structure is proposed.
		New crossover and mutation operators, namely graft crossover, splitting, merging, and alteration mutation, which are suitable for the quadtree structure, are introduced.
	www.cs.ualberta.ca /~yang/Projects/segmentation_using_genetic_algor.htm (226 words)

	Mathematical Apparatus of Genetic Algorithms - BaseGroup Labs
		In genetic algorithms an operator called crossing (also known as crossover or crossing over) is in charge of passing the attributes from parents to their offsprings.
		The next genetic operator is intended for maintaining the diversity of individuals in the population.
		Usually the highest limit of the algorithm functioning epochs is taken as such, or the algorithm is stopped upon stabilization of its convergence, normally measured by means of comparing the population’s fitness on various epochs.
	www.basegroup.ru /genetic/math.en.htm (1503 words)

	Genetic Algorithm
		The rest (from the same crossover point of parent 2 to its tail) is copied to the new offspring on the same positions.
		The binary string from beginning of parent 1 to its first crossover point and the binary string from its second crossover point to its end are copied to the new offspring.
		The rest (the first crossover point of parent 2 to its second crossover point) is copied to the new offspring in the same fashion.
	www.bridgeport.edu /sed/projects/449/Fall_2000/fangmin/chapter4.htm (1041 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.
		The crossover algorithm (see Figure 5) is used to breed a pair of children from a pair of parents.
		A random crossover point is chosen in each parent, and the subtree beneath these crossover points is swapped between the parents, creating the two offspring.
	msdn.microsoft.com /msdnmag/issues/04/08/GeneticAlgorithms/default.aspx (5755 words)

	Genetic Algorithms: Crossover
		Crossover is a genetic operator that combines (mates) two chromosomes (parents) to produce a new chromosome (offspring).
		The idea behind crossover is that the new chromosome may be better than both of the parents if it takes the best characteristics from each of the parents.
		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.
	www.nd.com /products/genetic/crossover.htm (525 words)

	Genetic Algorithm (Site not responding. Last check: 2007-10-14)
		Genetic algorithms perform a stochastic evolution process toward global optimization through the use of crossover and mutation operators.
		In order to avoid trapping in the local minimum that might result from adopting a simple crossover operator, we have added a heuristics-based mutation procedure to add diversity to the homepage population.
		When the GA search algorithm requests a mutated homepage, the system retrieves the top-ranked homepage from homepages in the user-specified category based on the keywords presented in the anchor homepages.
	ai.bpa.arizona.edu /~mramsey/papers/spider/node10.html (483 words)

	Finding Optimal Solutions Using A Hybrid GA-Heuristic Search Strategy (Site not responding. Last check: 2007-10-14)
		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)

	Genetic Algorithms Demo (Site not responding. Last check: 2007-10-14)
		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.
		Finally, a crossover operation is performed: Pairs of Eaters in the new population are chosen at random and have a certain probability of being "crossed over".
		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)

	Genetic Algorithms at daniel shiffman
		A genetic algorithm refers to the process of evolving populations of DNA-like data (encoded as a series of bits, 0s and 1s).
		Strictly speaking, a genetic algorithm involves the use of virtual DNA encoded as bits and follows a series of precise steps.
		Finally, we need two methods, one for “crossover” (a method that takes a given object’s current DNA sequence and combines it with another’s), and one for “mutation” (a method that mutates given characters in the sequence randomly, according to some probability).
	www.shiffman.net /teaching/the-nature-of-code/ga (1188 words)

	Parallel Genetic Algorithms and Airline Crew Scheduling
		Our objective in this work was to unify these factors by developing a parallel genetic algorithm and applying it to the solution of the set partitioning problem---a difficult combinatorial optimization problem used by many airlines as a mathematical model for assigning flight crews to flights.
		The parallel genetic algorithm we used is based on an island model where separate and isolated subpopulations evolve independently and in parallel.
		With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables.
	www-fp.mcs.anl.gov /ccst/research/reports_pre1998/algorithm_development/genetic_algorithm.html (995 words)

	Genetic Algorithms
		The former corresponds to ``non-deterministic polynomial'' algorithms (simply guess the correct solution and then confirm your answer), which is not the most practical way for us to solve problems.
		The method we use is crossover: we take the first part of one of the strings and match it with the second part of the other and vice versa.
		Unfortunately, genetic algorithms have not proved to be very successful in combinatorial optimization.
	mat.gsia.cmu.edu /mstc/nn/node1.html (921 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.
		GENETIC ALGORITHMs are used for a number of different application areas.
		When the GENETIC ALGORITHM is implemented it is usually done in a manner that involves the following cycle: Evaluate the FITNESS of all of the INDIVIDUALs in the POPULATION.
	www-2.cs.cmu.edu /Groups/AI/html/faqs/ai/genetic/part2/faq-doc-2.html (804 words)

	Genetic Algorithms and their applications (Site not responding. Last check: 2007-10-14)
		This talk will explain what a genetic algorithm is and give two examples of the application of genetic algorithms to real problems.
		The goal of the talk is to acquaint listeners with the genetic algorithm approach to evolutionary computation and, by example, to give them some idea of what such algorithms can and can not do.
		New population with a mutation and a crossover
	www.informatics.indiana.edu /fil/CAS/PPT/Davis/index.htm (185 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.
		It is important to realize that only two elements of the classical genetic algorithm need to be changed in order to apply the algorithm to a new problem: the representation of the individuals and the objective functions.
		One crossover thus creates two new individuals, called offspring: one containing the beginning portion of the first individual followed by the ending portion of the second individual, and another containing the beginning portion of the second individual followed by the ending portion of the first individual (Table 3).
	www.duke.edu /vertices/update/win95/genalg.html (2545 words)

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