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In computer science, Java Grammatical Evolution is an implementation of grammatical evolution in the Java programming language. Two examples include the jGE library and GEVA.
The jGE library was the first published implementation of grammatical evolution in the Java language. [1] Today, another well-known published Java implementation exists, named GEVA. GEVA was developed at University College Dublin's Natural Computing Research & Applications group under the guidance of one of the inventors of grammatical evolution, Dr. Michael O'Neill.
The jGE library aims to provide an implementation of grammatical evolution as well as an open-source, extendable, and free framework for experimentation in the area of evolutionary computation. Namely, it supports the implementation (through additions and extensions) of any evolutionary algorithm. [2] Its extendable architecture and design also facilitate the implementation and incorporation of new experimental implementations inspired by natural evolution and biology. [3]
The jGE library binary file, source code, documentation, and an extension for the NetLogo modeling environment, named jGE NetLogo extension, can be downloaded from the jGE Official Web Site.
The jGE library is free software released under the GNU General Public License v3.
A programming language is a system of notation for writing computer programs.
Prolog is a logic programming language that has its origins in artificial intelligence and computational linguistics.
In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.
A fitness function is a particular type of objective function that is used to summarise, as a single figure of merit, how close a given design solution is to achieving the set aims. Fitness functions are used in evolutionary algorithms (EA), such as genetic programming and genetic algorithms to guide simulations towards optimal design solutions.
The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.
In computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in programs. The value of a programming model can be judged on its generality: how well a range of different problems can be expressed for a variety of different architectures, and its performance: how efficiently the compiled programs can execute. The implementation of a parallel programming model can take the form of a library invoked from a sequential language, as an extension to an existing language, or as an entirely new language.
Frame semantics is a theory of linguistic meaning developed by Charles J. Fillmore that extends his earlier case grammar. It relates linguistic semantics to encyclopedic knowledge. The basic idea is that one cannot understand the meaning of a single word without access to all the essential knowledge that relates to that word. For example, one would not be able to understand the word "sell" without knowing anything about the situation of commercial transfer, which also involves, among other things, a seller, a buyer, goods, money, the relation between the money and the goods, the relations between the seller and the goods and the money, the relation between the buyer and the goods and the money and so on. Thus, a word activates, or evokes, a frame of semantic knowledge relating to the specific concept to which it refers.
Concurrent computing is a form of computing in which several computations are executed concurrently—during overlapping time periods—instead of sequentially—with one completing before the next starts.
This is an alphabetical list of articles pertaining specifically to software engineering.
A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence.
Grammatical evolution (GE) is an evolutionary computation and, more specifically, a genetic programming (GP) technique (or approach) pioneered by Conor Ryan, JJ Collins and Michael O'Neill in 1998 at the BDS Group in the University of Limerick.
In computing, algorithmic skeletons, or parallelism patterns, are a high-level parallel programming model for parallel and distributed computing.
Natural computing, also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials to compute. The main fields of research that compose these three branches are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, and quantum computing, among others.