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Anti-unification : Jacobsen, Erik (Jun 1991), Unification and Anti-Unification (PDF), Technical Report Østvold, Bjarte M. (Apr 2004), A Functional Reconstruction of Anti-Unification (PDF), NR Note, vol. DART/04/04, Norwegian Computing Center Boytcheva, Svetla; Markov, Zdravko (2002). "An Algorithm for Inducing Least Ge... |
Anti-unification : Calculus of constructions: Pfenning, Frank (Jul 1991). "Unification and Anti-Unification in the Calculus of Constructions" (PDF). Proc. 6th LICS. Springer. pp. 74–85. Simply typed lambda calculus (Input: Terms in the eta-long beta-normal form. Output: higher-order patterns): Baumgartner, Alexander; K... |
Anti-unification : == References == |
First-order inductive learner : In machine learning, first-order inductive learner (FOIL) is a rule-based learning algorithm. |
First-order inductive learner : Developed in 1990 by Ross Quinlan, FOIL learns function-free Horn clauses, a subset of first-order predicate calculus. Given positive and negative examples of some concept and a set of background-knowledge predicates, FOIL inductively generates a logical concept definition or rule for th... |
First-order inductive learner : The FOIL algorithm is as follows: Input List of examples and predicate to be learned Output A set of first-order Horn clauses FOIL(Pred, Pos, Neg) Let Pos be the positive examples Let Pred be the predicate to be learned Until Pos is empty do: Let Neg be the negative examples Set Body to ... |
First-order inductive learner : Suppose FOIL's task is to learn the concept grandfather(X,Y) given the relations father(X,Y) and parent(X,Y). Furthermore, suppose our current Body consists of grandfather(X,Y) ← parent(X,Z). This can be extended by conjoining Body with any of the literals father(X,X), father(Y,Z), paren... |
First-order inductive learner : The FOCL algorithm (First Order Combined Learner) extends FOIL in a variety of ways, which affect how FOCL selects literals to test while extending a clause under construction. Constraints on the search space are allowed, as are predicates that are defined on a rule rather than on a set ... |
First-order inductive learner : http://www.csc.liv.ac.uk/~frans/KDD/Software/FOIL_PRM_CPAR/foil.html |
Progol : Progol is an implementation of inductive logic programming that combines inverse entailment with general-to-specific search through a refinement graph. |
Progol : Inverse entailment is used with mode declarations to derive the bottom clause, the most-specific clause within the mode language which subsume a given example. This clause is used to guide a refinement-graph search. Unlike the searches of Ehud Shapiro's model inference system (MIS) and J. Ross Quinlan's FOIL, ... |
Progol : Progol was introduced by Stephen Muggleton in 1995. In 1996, it was used by Ashwin Srinivasan, Muggleton, Michael Sternberg and Ross King to predict the mutagenic activity in nitroaromatic compounds. This was considered a landmark application for inductive logic programming, as a general purpose inductive lear... |
Theta-subsumption : Theta-subsumption (θ-subsumption, or just subsumption) is a decidable relation between two first-order clauses that guarantees that one clause logically entails the other. It was first introduced by John Alan Robinson in 1965 and has become a fundamental notion in inductive logic programming. Decidi... |
Theta-subsumption : A clause, that is, a disjunction of first-order literals, can be considered as a set containing all its disjuncts. With this convention, a clause c 1 θ-subsumes a clause c 2 if there is a substitution θ such that the clause obtained by applying θ to c 1 is a subset of c 2 . |
Theta-subsumption : θ-subsumption is a weaker relation than logical entailment, that is, whenever a clause c 1 θ-subsumes a clause c 2 , then c 1 logically entails c 2 . However, the converse is not true: A clause can logically entail another clause, but not θ-subsume it. θ-subsumption is decidable; more precisely,... |
Theta-subsumption : θ-subsumption was first introduced by J. Alan Robinson in 1965 in the context of resolution, and was first applied to inductive logic programming by Gordon Plotkin in 1970 for finding and reducing least general generalisations of sets of clauses. In 1977, Lewis D. Baxter proves that θ-subsumption is... |
Theta-subsumption : Theorem provers based on the resolution or superposition calculus use θ-subsumption to prune redundant clauses. In addition, θ-subsumption is the most prominent notion of entailment used in inductive logic programming, where it is the fundamental tool to determine whether one clause is a specialisat... |
Theta-subsumption : Baxter, Lewis Denver (September 1977). The complexity of unification (PDF) (Thesis). University of Waterloo. De Raedt, Luc (2008), Logical and Relational Learning, Cognitive Technologies, Berlin, Heidelberg: Springer, Bibcode:2008lrl..book.....D, doi:10.1007/978-3-540-68856-3, ISBN 978-3-540-20040-6... |
Genetic programming : Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs. It applies the genetic operators selection according to a predefined fitness measure, mutation and crossover. The crossover operation... |
Genetic programming : The first record of the proposal to evolve programs is probably that of Alan Turing in 1950. There was a gap of 25 years before the publication of John Holland's 'Adaptation in Natural and Artificial Systems' laid out the theoretical and empirical foundations of the science. In 1981, Richard Forsy... |
Genetic programming : GP has been successfully used as an automatic programming tool, a machine learning tool and an automatic problem-solving engine. GP is especially useful in the domains where the exact form of the solution is not known in advance or an approximate solution is acceptable (possibly because finding th... |
Genetic programming : Meta-genetic programming is the proposed meta-learning technique of evolving a genetic programming system using genetic programming itself. It suggests that chromosomes, crossover, and mutation were themselves evolved, therefore like their real life counterparts should be allowed to change on thei... |
Genetic programming : Bio-inspired computing Covariance Matrix Adaptation Evolution Strategy (CMA-ES) Evolutionary image processing Fitness approximation Genetic improvement Genetic representation Grammatical evolution Inductive programming Linear genetic programming Multi expression programming Propagation of schema |
Genetic programming : Aymen S Saket & Mark C Sinclair Genetic Programming and Evolvable Machines, a journal Evo2 for genetic programming GP bibliography The Hitch-Hiker's Guide to Evolutionary Computation Riccardo Poli, William B. Langdon, Nicholas F. McPhee, John R. Koza, "A Field Guide to Genetic Programming" (2008) ... |
Cartesian genetic programming : Cartesian genetic programming is a form of genetic programming that uses a graph representation to encode computer programs. It grew from a method of evolving digital circuits developed by Julian F. Miller and Peter Thomson in 1997. The term ‘Cartesian genetic programming’ first appeared... |
Cartesian genetic programming : Genetic programming Gene expression programming Grammatical evolution Linear genetic programming Multi expression programming == References == |
Grammatical evolution : Grammatical evolution (GE) is a genetic programming (GP) technique (or approach) from evolutionary computation pioneered by Conor Ryan, JJ Collins and Michael O'Neill in 1998 at the BDS Group in the University of Limerick. As in any other GP approach, the objective is to find an executable progr... |
Grammatical evolution : In type-free, conventional Koza-style GP, the function set must meet the requirement of closure: all functions must be capable of accepting as their arguments the output of all other functions in the function set. Usually, this is implemented by dealing with a single data-type such as double-pre... |
Grammatical evolution : GE offers a solution to the single-type limitation by evolving solutions according to a user-specified grammar (usually a grammar in Backus-Naur form). Therefore the search space can be restricted, and domain knowledge of the problem can be incorporated. The inspiration for this approach comes f... |
Grammatical evolution : Despite its successes, GE has been the subject of some criticism. One issue is that as a result of its mapping operation, GE's genetic operators do not achieve high locality which is a highly regarded property of genetic operators in evolutionary algorithms. |
Grammatical evolution : Although GE was originally described in terms of using an Evolutionary Algorithm, specifically, a Genetic Algorithm, other variants exist. For example, GE researchers have experimented with using particle swarm optimization to carry out the searching instead of genetic algorithms with results co... |
Grammatical evolution : GE was originally a combination of the linear representation as used by the Genetic Algorithm for Developing Software (GADS) and Backus Naur Form grammars, which were originally used in tree-based GP by Wong and Leung in 1995 and Whigham in 1996. Other related work noted in the original GE paper... |
Grammatical evolution : There are several implementations of GE. These include the following. |
Grammatical evolution : Genetic programming Java Grammatical Evolution Cartesian genetic programming Gene expression programming Linear genetic programming Multi expression programming |
Linear genetic programming : "Linear genetic programming" is unrelated to "linear programming". Linear genetic programming (LGP) is a particular method of genetic programming wherein computer programs in a population are represented as a sequence of register-based instructions from an imperative programming language or... |
Linear genetic programming : Because LGP programs are basically represented by a linear sequence of instructions, they are simpler to read and to operate on than their tree-based counterparts. For example, a simple program written to solve a Boolean function problem with 3 inputs (in R1, R2, R3) and one output (in R0),... |
Linear genetic programming : Multi expression programming Cartesian genetic programming Grammatical evolution Genetic programming |
Linear genetic programming : Slash/A A programming language and C++ library specifically designed for linear GP DigitalBiology.NET Vertical search engine for GA/GP resources Discipulus Genetic-Programming Software MicroGP Genetic-Programming Software (open source) [1] An open-source Linear GP project based on a Java-ba... |
Joseph Nechvatal : Joseph Nechvatal (born January 15, 1951): 1176 is an American post-conceptual digital artist and art theoretician who creates computer-assisted paintings and computer animations, often using custom computer viruses. |
Joseph Nechvatal : Joseph Nechvatal was born in Chicago.: 1176 He studied fine art and philosophy at Southern Illinois University Carbondale, Cornell University and Columbia University.: 1176 He earned a Doctor of Philosophy in Philosophy of Art and Technology at the Planetary Collegium at University of Wales, Newport ... |
Joseph Nechvatal : John Johnston, The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press, 2008, cover Donald Kuspit, The Matrix of Sensations VI: Digital Artists and the New Creative Renaissance Joline Blais and Jon Ippolito, The Edge of Art, Thames & Hudson Ltd, p. 213 Frank Popper, From ... |
Joseph Nechvatal : Joseph Nechvatal's website |
Santa Fe Trail problem : The Santa Fe Trail problem is a genetic programming exercise in which artificial ants search for food pellets according to a programmed set of instructions. The layout of food pellets in the Santa Fe Trail problem has become a standard for comparing different genetic programming algorithms and ... |
Santa Fe Trail problem : Genetic programming Agent-based model Java Grammatical Evolution |
Santa Fe Trail problem : Genetic-programming.org Grammatical-evolution.org Teamwork in genetic programming java Grammatical Evolution |
Symbolic regression : Symbolic regression (SR) is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity. No particular model is provided as a starting point for symbolic regression. Instead, initial ex... |
Symbolic regression : While conventional regression techniques seek to optimize the parameters for a pre-specified model structure, symbolic regression avoids imposing prior assumptions, and instead infers the model from the data. In other words, it attempts to discover both model structures and model parameters. This ... |
Symbolic regression : Most symbolic regression algorithms prevent combinatorial explosion by implementing evolutionary algorithms that iteratively improve the best-fit expression over many generations. Recently, researchers have proposed algorithms utilizing other tactics in AI. Silviu-Marian Udrescu and Max Tegmark de... |
Symbolic regression : Closed-form expression § Conversion from numerical forms Genetic programming Gene expression programming Kolmogorov complexity Linear genetic programming Mathematical optimization Multi expression programming Regression analysis Reverse mathematics Discovery system (AI research) |
Symbolic regression : Mark J. Willis; Hugo G. Hiden; Ben McKay; Gary A. Montague; Peter Marenbach (1997). "Genetic programming: An introduction and survey of applications" (PDF). IEE Conference Publications. IEE. pp. 314–319. Wouter Minnebo; Sean Stijven (2011). "Chapter 4: Symbolic Regression" (PDF). Empowering Knowle... |
Symbolic regression : Ivan Zelinka (2004). "Symbolic regression — an overview". Hansueli Gerber (1998). "Simple Symbolic Regression Using Genetic Programming". (Java applet) — approximates a function by evolving combinations of simple arithmetic operators, using algorithms developed by John Koza. Katya Vladislavleva. "... |
Evolutionary algorithm : Evolutionary algorithms (EA) reproduce essential elements of the biological evolution in a computer algorithm in order to solve “difficult” problems, at least approximately, for which no exact or satisfactory solution methods are known. They belong to the class of metaheuristics and are a subse... |
Evolutionary algorithm : The following is an example of a generic evolutionary algorithm: Randomly generate the initial population of individuals, the first generation. Evaluate the fitness of each individual in the population. Check, if the goal is reached and the algorithm can be terminated. Select individuals as par... |
Evolutionary algorithm : Similar techniques differ in genetic representation and other implementation details, and the nature of the particular applied problem. Genetic algorithm – This is the most popular type of EA. One seeks the solution of a problem in the form of strings of numbers (traditionally binary, although ... |
Evolutionary algorithm : The following theoretical principles apply to all or almost all EAs. |
Evolutionary algorithm : The areas in which evolutionary algorithms are practically used are almost unlimited and range from industry, engineering, complex scheduling, agriculture, robot movement planning and finance to research and art. The application of an evolutionary algorithm requires some rethinking from the ine... |
Evolutionary algorithm : There are some other proven and widely used methods of nature inspired global search techniques such as Memetic algorithm – A hybrid method, inspired by Richard Dawkins's notion of a meme. It commonly takes the form of a population-based algorithm (frequently an EA) coupled with individual lear... |
Evolutionary algorithm : In 2020, Google stated that their AutoML-Zero can successfully rediscover classic algorithms such as the concept of neural networks. The computer simulations Tierra and Avida attempt to model macroevolutionary dynamics. |
Evolutionary algorithm : Ashlock, D. (2006), Evolutionary Computation for Modeling and Optimization, Springer, New York, doi:10.1007/0-387-31909-3 ISBN 0-387-22196-4. Bäck, T. (1996), Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford Univ. Press, ... |
Evolutionary algorithm : An Overview of the History and Flavors of Evolutionary Algorithms |
Crossover (evolutionary algorithm) : Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, a... |
Crossover (evolutionary algorithm) : Traditional genetic algorithms store genetic information in a chromosome represented by a bit array. Crossover methods for bit arrays are popular and an illustrative example of genetic recombination. |
Crossover (evolutionary algorithm) : For the crossover operators presented above and for most other crossover operators for bit strings, it holds that they can also be applied accordingly to integer or real-valued genomes whose genes each consist of an integer or real-valued number. Instead of individual bits, integer ... |
Crossover (evolutionary algorithm) : For combinatorial tasks, permutations are usually used that are specifically designed for genomes that are themselves permutations of a set. The underlying set is usually a subset of N or N 0 _ . If 1- or n-point or uniform crossover for integer genomes is used for such genomes, ... |
Crossover (evolutionary algorithm) : Evolutionary algorithm Genetic representation Fitness function Selection (genetic algorithm) |
Crossover (evolutionary algorithm) : John Holland (1975). Adaptation in Natural and Artificial Systems, PhD thesis, University of Michigan Press, Ann Arbor, Michigan. ISBN 0-262-58111-6. Schwefel, Hans-Paul (1995). Evolution and Optimum Seeking. New York: John Wiley & Sons. ISBN 0-471-57148-2. Davis, Lawrence (1991). H... |
Crossover (evolutionary algorithm) : Newsgroup: comp.ai.genetic FAQ - see section on crossover (also known as recombination). |
Genetic operator : A genetic operator is an operator used in evolutionary algorithms (EA) to guide the algorithm towards a solution to a given problem. There are three main types of operators (mutation, crossover and selection), which must work in conjunction with one another in order for the algorithm to be successful... |
Genetic operator : Genetic variation is a necessity for the process of evolution. Genetic operators used in evolutionary algorithms are analogous to those in the natural world: survival of the fittest, or selection; reproduction (crossover, also called recombination); and mutation. |
Genetic operator : While each operator acts to improve the solutions produced by the evolutionary algorithm working individually, the operators must work in conjunction with each other for the algorithm to be successful in finding a good solution. Using the selection operator on its own will tend to fill the solution p... |
Mutation (evolutionary algorithm) : Mutation is a genetic operator used to maintain genetic diversity of the chromosomes of a population of an evolutionary algorithm (EA), including genetic algorithms in particular. It is analogous to biological mutation. The classic example of a mutation operator of a binary coded gen... |
Mutation (evolutionary algorithm) : The mutation of bit strings ensue through bit flips at random positions. Example: The probability of a mutation of a bit is 1 l , where l is the length of the binary vector. Thus, a mutation rate of 1 per mutation and individual selected for mutation is reached. |
Mutation (evolutionary algorithm) : Many EAs, such as the evolution strategy or the real-coded genetic algorithms, work with real numbers instead of bit strings. This is due to the good experiences that have been made with this type of coding. The value of a real-valued gene can either be changed or redetermined. A mut... |
Mutation (evolutionary algorithm) : Mutations of permutations are specially designed for genomes that are themselves permutations of a set. These are often used to solve combinatorial tasks. In the two mutations presented, parts of the genome are rotated or inverted. |
Mutation (evolutionary algorithm) : Evolutionary algorithms Genetic algorithms Evolution strategy Genetic programming Evolutionary programming |
Mutation (evolutionary algorithm) : John Holland (1975). Adaptation in Natural and Artificial Systems, PhD thesis, University of Michigan Press, Ann Arbor, Michigan. ISBN 0-262-58111-6. Schwefel, Hans-Paul (1995). Evolution and Optimum Seeking. New York: John Wiley & Sons. ISBN 0-471-57148-2. Davis, Lawrence (1991). Ha... |
Artificial development : Artificial development, also known as artificial embryogeny or machine intelligence or computational development, is an area of computer science and engineering concerned with computational models motivated by genotype–phenotype mappings in biological systems. Artificial development is often co... |
Artificial development : Rene Doursat, "Organically grown architectures: Creating decentralized, autonomous systems by embryomorphic engineering", Organic Computing, R. P. Würtz, (ed.), Springer-Verlag, Ch. 8, pp. 167-200, 2008. Guo, H., Y. Meng and Y. Jin (2009). "A cellular mechanism for multi-robot construction via ... |
Cellular evolutionary algorithm : A cellular evolutionary algorithm (cEA) is a kind of evolutionary algorithm (EA) in which individuals cannot mate arbitrarily, but every one interacts with its closer neighbors on which a basic EA is applied (selection, variation, replacement). The cellular model simulates natural evol... |
Cellular evolutionary algorithm : A cellular evolutionary algorithm (cEA) usually evolves a structured bidimensional grid of individuals, although other topologies are also possible. In this grid, clusters of similar individuals are naturally created during evolution, promoting exploration in their boundaries, while ex... |
Cellular evolutionary algorithm : In a regular synchronous cEA, the algorithm proceeds from the very first top left individual to the right and then to the several rows by using the information in the population to create a new temporary population. After finishing with the bottom-right last individual the temporary po... |
Cellular evolutionary algorithm : Cellular EAs are very amenable to parallelism, thus usually found in the literature of parallel metaheuristics. In particular, fine grain parallelism can be used to assign independent threads of execution to every individual, thus allowing the whole cEA to run on a concurrent or actual... |
Cellular evolutionary algorithm : Cellular automaton Dual-phase evolution Enrique Alba Evolutionary algorithm Metaheuristic Parallel metaheuristic |
Cellular evolutionary algorithm : E. Alba, B. Dorronsoro, Cellular Genetic Algorithms, Springer-Verlag, ISBN 978-0-387-77609-5, 2008 A.J. Neighbor, J.J. Durillo, F. Luna, B. Dorronsoro, E. Alba, MOCell: A New Cellular Genetic Algorithm for Multiobjective Optimization, International Journal of Intelligent Systems, 24:72... |
Cellular evolutionary algorithm : The site on Cellular Evolutionary Algorithms NEO Research Group at University of Málaga, Spain Archived 2018-09-28 at the Wayback Machine |
Chromosome (evolutionary algorithm) : A chromosome or genotype in evolutionary algorithms (EA) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying to solve. The set of all solutions, also called individuals according to the biological model, is known as the p... |
Chromosome (evolutionary algorithm) : When creating the genetic representation of a task, it is determined which decision variables and other degrees of freedom of the task should be improved by the EA and possible additional heuristics and how the genotype-phenotype mapping should look like. The design of a chromosome... |
Chromosome (evolutionary algorithm) : Thomas Bäck (1996): Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford Univ. Press. ISBN 978-0-19-509971-3 Wolfgang Banzhaf, P. Nordin, R. Keller, F. Francone (1998): Genetic Programming - An Introduction, Morg... |
Cultural algorithm : Cultural algorithms (CA) are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the population component. In this sense, cultural algorithms can be seen as an extension to a conventional genetic algorithm. Cultural algorithms wer... |
Cultural algorithm : The belief space of a cultural algorithm is divided into distinct categories. These categories represent different domains of knowledge that the population has of the search space. The belief space is updated after each iteration by the best individuals of the population. The best individuals can b... |
Cultural algorithm : The population component of the cultural algorithm is approximately the same as that of the genetic algorithm. |
Cultural algorithm : Cultural algorithms require an interface between the population and belief space. The best individuals of the population can update the belief space via the update function. Also, the knowledge categories of the belief space can affect the population component via the influence function. The influe... |
Cultural algorithm : Initialize population space (choose initial population) Initialize belief space (e.g. set domain specific knowledge and normative value-ranges) Repeat until termination condition is met Perform actions of the individuals in population space Evaluate each individual by using the fitness function Sel... |
Cultural algorithm : Various optimization problems Social simulation Real-parameter optimization |
Cultural algorithm : Artificial intelligence Artificial life Evolutionary computation Genetic algorithm Harmony search Machine learning Memetic algorithm Memetics Metaheuristic Social simulation Sociocultural evolution Stochastic optimization Swarm intelligence |
Cultural algorithm : Robert G. Reynolds, Ziad Kobti, Tim Kohler: Agent-Based Modeling of Cultural Change in Swarm Using Cultural Algorithms R. G. Reynolds, “An Introduction to Cultural Algorithms, ” in Proceedings of the 3rd Annual Conference on Evolutionary Programming, World Scientific Publishing, pp 131–139, 1994. R... |
Defining length : In genetic algorithms and genetic programming defining length L(H) is the maximum distance between two defining symbols (that is symbols that have a fixed value as opposed to symbols that can take any value, commonly denoted as # or *) in schema H. In tree GP schemata, L(H) is the number of links in t... |
Defining length : Schemata "00##0", "1###1", "01###", and "##0##" have defining lengths of 4, 4, 1, and 0, respectively. Lengths are computed by determining the last fixed position and subtracting from it the first fixed position. In genetic algorithms as the defining length of a solution increases so does the suscepti... |
Differential evolution : Differential evolution (DE) is an evolutionary algorithm to optimize a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such methods are commonly known as metaheuristics as they make few or no assumptions about the optimized problem and ca... |
Differential evolution : Storn and Price introduced Differential Evolution in 1995. Books have been published on theoretical and practical aspects of using DE in parallel computing, multiobjective optimization, constrained optimization, and the books also contain surveys of application areas. Surveys on the multi-facet... |
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