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Topic: Genetic programming

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	gpanimatedtutorial
		Genetic programming is problem-independent in the sense that the flowchart specifying the basic sequence of executional steps is not modified for each new run or each new problem.
		The individual programs in the initial population are typically generated by recursively generating a rooted point-labeled program tree composed of random choices of the primitive functions and terminals (provided by the human user as part of the first and second preparatory steps of a run of genetic programming).
		Programs with architectures that are well-suited to the problem at hand will tend to grow and prosper in the competitive evolutionary process, while programs with inadequate architectures will tend to wither away under the relentless selective pressure of the problem's fitness measure.
	www.genetic-programming.com /gpflowchart.html (2085 words)

	Genetic programming - Wikipedia, the free encyclopedia
		Genetic programming (GP) is an automated methodology inspired by biological evolution to find computer programs that best perform a user-defined task.
		It is therefore a particular machine learning technique that uses an evolutionary algorithm to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.
		In the early (and traditional) implementations of GP, program instructions and data values were organized in tree-structures, thus favoring the use of languages that naturally embody such a structure (an important example pioneered by Koza is Lisp).
	en.wikipedia.org /wiki/Genetic_programming (740 words)

	Glossary - Data Structures and Genetic Programming
		In genetic programming the individual and its representation are usually the same, both being the program parse tree.
		Genetic Algorithm A population containing a number of trial solutions each of which is evaluated (to yield a fitness) and a new generation is created from the better of them.
		Genetic Operator An operator in a genetic algorithm or genetic programming, which acts upon the chromosome to produce a new individual.
	www.cs.bham.ac.uk /~wbl/thesis.glossary.html (1661 words)

	Define genetic programming - a definition from Whatis.com (Site not responding. Last check: )
		Genetic programming is a model of programming which uses the ideas (and some of the terminology) of biological evolution to handle a complex problem.
		Genetic programming is an approach that seems most appropriate with problems in which there are a large number of fluctuating variables such as those related to artificial intelligence.
		Genetic programming can be viewed as an extension of the genetic algorithm, a model for testing and selecting the best choice among a set of results, each represented by a string.
	whatis.techtarget.com /definition/0,,sid9_gci214639,00.html (336 words)

	Genetic Programming
		Genetic programming is a form of artificial intelligence programming that deals specifically with the ability of computer programs to modify themselves to more effectively solve the problems they address.
		Genetic programming thus seems to have the potential to solve more sophisticated problems where the answer might not be accessible to point-to point hill climbing algorithms (Seven Differences 3).
		Although much of the available research describes experiments where genetic programming is applied to classic optimization problems or game theory, Koza and his colleagues claim that their genetic programming experiments have actually achieved results similar to what human effort could create.
	home.comcast.net /~dean_Ennis/genetic_programming.html (1602 words)

	Parallel Genetic Programming Induction of Binary Decision Diagrams
		Genetic algorithms (GAs) [11] seek optimal or near-optimal solutions to hard search and learning problems by giving more chances of survival to fitter individuals in an evolving population in which each individual represents a feasible solution to the given problem through a suitably coded string of symbols.
		Genetic programming (GP) is a variation of genetic algorithms in which the evolving individuals are themselves computer programs instead of fixed length strings from a limited alphabet of symbols [6].
		In preparing to use genetic programming to solve a problem one has to decide on the set of terminals, the set of primitive functions, the fitness measure, the stopping criterion and the values of some parameters such as population size and crossover rate [6].
	sawww.epfl.ch /SIC/SA/publications/SCR95/7-95-24a.html (2603 words)

	gpanimatedtutorial
		Genetic programming addresses this challenge by providing a method for automatically creating a working computer program from a high-level problem statement of the problem.
		Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a problem.
		Genetic programming iteratively transforms a population of computer programs into a new generation of the population by applying analogs of naturally occurring genetic operations.
	www.genetic-programming.com /gpanimatedtutorial.html (854 words)

	[No title]
		Genetic programming and genetic algorithms are two different evolutionary algorithms.
		While this implementation meets all of the essential requirements to qualify as genetic programming, in its present state there is plenty of room for the solution to be augmented and improved.
		I'll also give you an overview of genetic programming and show you how ants can be evolved to walk a more complex trail, as well as how you can make changes to the problem class to make new operations available.
	msdn.microsoft.com /msdnmag/issues/04/08/GeneticAlgorithms/default.aspx (5662 words)

	genetic programming@Everything2.com
		Programs are expressed as virtual "genes" and, using a genetic algorithm, compete with each other in program space - i.e.
		Genetic programs tend to be represented as binary trees.
		To solve a problem using genetic programming, you simply make a load of randomly generated programs and then mutate and breed them depending on how good they are.
	everything2.com /index.pl?node_id=95847 (1047 words)

	Genetic programming
		Genetic programming, invented by Cramer in 1985 (Cramer 1985) and further developed by Koza (1992), solves the problem of fixed length solutions by creating non-linear entities with different sizes and shapes.
		The individuals of genetic programming are usually LISP programs represented as parse trees (Figure 1.8).
		Also worth mentioning is that, in GP, the genetic operators act directly on the parse tree and, although at first sight this might appear advantageous, it greatly limits this technique (it is impossible to make an orange tree produce mangos only by grafting and pruning).
	www.gene-expression-programming.com /GepBook/Chapter1/Section5.htm (855 words)

	Salon Technology \| Software that writes software (Site not responding. Last check: )
		Darwin United was created "automatically," using a form of computer programming known as genetic programming, or GP.
		And genetic programming may be exactly the type of breakthrough technology the RoboCup federation had in mind.
		GP programs, which can handle tasks ranging from robot-like motion to human-like inventions, aren't written in the same way that traditional software is written.
	www.salon.com /tech/feature/1999/08/10/genetic_programming (677 words)

	Wall Following Algorithms using Genetic Programming
		The performance of a computer program in GP can be a function of the final result that it produces, or it can be based on an analysis of some side effect that the computer program has produced.
		All experiments are conducted using GP Experimenter, a genetic programming environment and tool set developed by the author.
		Although genetic programming is advertised as creating human-readable solutions, it is understood in the GP community that this is only partly true.
	www.seattlerobotics.org /encoder/may98/genprog.html (3773 words)

	genetic-programming.org-Home-Page
		Genetic programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem.
		In addition, genetic programming can automatically create, in a single run, a general (parameterized) solution to a problem in the form of a graphical structure whose nodes or edges represent components and where the parameter values of the components are specified by mathematical expressions containing free variables.
		The technique of genetic programming (GP) is one of the techniques of the field of genetic and evolutionary computation (GEC) which, in turn, includes techniques such as genetic algorithms (GA), evolution strategies (ES), evolutionary programming (EP), grammatical evolution (GE), and machine code (linear genome) genetic programming.
	www.genetic-programming.org (2530 words)

	Evolving Soccer Softbots (Site not responding. Last check: )
		However, Maryland's Genetic Programming entry in in fact beat its first two competitors (5-2 against U British Columbia, Canada and 17-0 over Toyohashi University of Science and Technology, Japan) before losing to University of Tokyo (last year's champion, 6-1) and subsequently Tokyo Institute of Technology (16-4) in the single-elimination round.
		Genetic Programming (GP) is an offshoot of Genetic Algorithms (GAs) whose aim is to evolve a function or algorithm to best solve some task (GAs, in contrast, just evolve some optimal combination of variables).
		A paper using Genetic Programming (Symbolic Regression) to compute the probability of receiving a passed ball given intelligent opponents in the field.
	www.cs.umd.edu /users/seanl/soccerbots (1221 words)

	Using Genetic Programming to Play Mancala
		Genetic programming is an automatic programming technique - that is, a method for generating computer programs other than having a human write them - inspired by and roughly modeled on biological evolution.
		John R. Koza of Stanford University is generally considered the inventor of genetic programming, although there were a number of related techniques that preceded his discovery, most notably genetic algorithms (which were invented by John Holland at the University of Michigan in 1975).
		To evolve a program that does well against a wide variety of opponents, it may be necessary for the fitness function to include playing against a number of different opponents.
	www.corngolem.com /john/gp/project.html (1703 words)

	GameDev.net - Application of Genetic Programming to the Snake Game
		Genetic programming (GP), however, has been proven to allow a computer to create human-competitive results.
		In an original approach to demonstrating the effectiveness of GP at producing human-competitive results, this paper describes the evolution of a genetic program that can successfully achieve the maximum possible score in the "snake game." The problem posed by the snake game is of particular interest for two main reasons.
		In evolving a genetic program to successfully eat the maximum amount of food, a human competitive solution, in terms of score, will have been obtained.
	www.gamedev.net /reference/articles/article1175.asp (7029 words)

	Genetic Programming - John (ResearchIndex) (Site not responding. Last check: )
		Abstract: Introduction Genetic programming is a domain-independent problem-solving approach in which computer programs are evolved to solve, or approximately solve, problems.
		Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring genetic operations such as crossover (sexual recombination) and mutation.
		Genetic Programming (context) - Peter, bias et al.
	citeseer.ist.psu.edu /212034.html (950 words)

	Advances in Genetic Programming - The MIT Press
		Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior.
		Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and "demes" to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality.
		Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition.
	mitpress.mit.edu /book-home.tcl?isbn=0262111888 (283 words)

	Description of Genetic Programming (Site not responding. Last check: )
		In a standard genetic program, the representation used is a variable-sized tree of functions and values.
		Genetic algorithms and genetic programming are similar in most other aspects, except that the reproduction operators are tailored to a tree representation.
		In a standard genetic program, all values and functions are assumed to return the same type, although functions may vary in the number of arguments they take.
	neo.lcc.uma.es /cEA-web/GP.htm (247 words)

	Genetic Programming (Site not responding. Last check: )
		As such, it is easily represented as a typical program tree of genetic programming [349] .
		Genetic programming operates on a population of such individuals.
		In our application of genetic programming we use the standard set of functions as used in symbolic regression studies done with genetic programming.
	www.uwasa.fi /cs/publications/2NWGA/node74.html (253 words)

	Genetic Programming in C/C++
		Genetic programming is a relatively new form of artificial intelligence, and is based on the ideas of Darwinian evolution and genetics.
		The foremost work in genetic programming is John Koza's Genetic Programming, which describes a set of LISP routines which modify randomly generated LISP strings.
		In this paper, We attempt to explain the paradigm of genetic programming, and its implementation in the C programming language.
	www.cis.upenn.edu /~hollick/genetic/paper2.html (86 words)

	Genetic Programming
		Programs are combined or mutated into offspring, which aree Äadded to the next generation of programs.
		Genetic programming has been used for applications where the real reason to evolve programs is to enjoy the output from the programs, i.e., the artefacts produced by the programs are more interesting than the programs themselves.
		One criticism of GP approaches is that the programs produced are too large and complicated to be understood, so, if being able to understand the resulting programs is a consideration, the length of the programs should be kept relatively small.
	www.doc.ic.ac.uk /~sgc/teaching/v231/lecture17.html (3364 words)

	Genetic Programming : An Introduction : On the Automatic Evolution of Computer Programs and Its Applications (The ... (Site not responding. Last check: )
		Later chapters define what genetic programming is and what strategies it uses to let computers program themselves.
		The authors also examine the state of the art of genetic programming and define what problems need to be solved before it can be widely adopted.
		A later chapter on applications that use genetic programming offers dozens of papers, with applications of this approach from a wide variety of fields, including biology, industry, and computers (and some impressive technologies such as robotics and data mining).
	www.crimsonbird.com /cgi-bin/a.cgi?j=155860510X (605 words)

	Genetic Programming
		In genetic programming, normal genetic operations of crossover, mutation and reproduction are applied to computer code to create steadily improving and functioning computer code.
		Genetic systems explore multiple paths simultaneously; each individual in a population is a potential solution to the problems the environment poses.
		Genetic Programming Inc. John Koza is a pioneer in the field of genetic programming.
	www.daxtron.com /geneticprogramming.htm (840 words)

	An Analysis of Diversity in Genetic Programming (Site not responding. Last check: )
		Genetic programming is a metaheuristic search method that uses a population of variable-length computer programs and a search strategy based on biological evolution.
		The population is related to many key aspects of the genetic programming algorithm.
		Currently, genetic programming is applied to a wide range of problems under many varied contexts.
	www.cs.nott.ac.uk /~smg/thesis_html (316 words)

	Open Directory - Computers: Artificial Intelligence: Genetic Programming: Algorithms
		Genetic Algorithm Experiment - This Java applet demonstrates a continuous value genetic algorithm on a variety of problem spaces with a variety of reproduction methods.
		Genetic Algorithms for Squeak - This GA framework in Squeak implements the operation of selection, mutation and crossing-over with visualization features.
		Genetic and Evolutionary Algorithm Toolbox - GEATbx is a comprehensive implementation of evolutionary algorithms in Matlab.
	dmoz.org /Computers/Artificial_Intelligence/Genetic_Programming/Algorithms (1048 words)

	Bruce Edmonds - The Uses of Genetic Programming in Social Simulation: A Review of Five Books
		GP is an extension of the Genetic Algorithm which was invented by John Holland (1975).
		Genetic Programming is one technique (albeit an important one) amongst a whole range of possible evolutionary algorithms.
		(Genetic Algorithms encode their candidate solutions into fixed length strings.) On the other hand, GP requires more computational power than the GA. The critical factors in using GP are the computational time taken to evaluate a solution once, the size of your population and the number of generations you intend to run the GP for.
	jasss.soc.surrey.ac.uk /2/1/review1.html (4967 words)

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