In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.
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.
A digital organism is a self-replicating computer program that mutates and evolves. Digital organisms are used as a tool to study the dynamics of Darwinian evolution, and to test or verify specific hypotheses or mathematical models of evolution. The study of digital organisms is closely related to the area of artificial life.
Interactive evolutionary computation (IEC) or aesthetic selection is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known or the result of optimization should fit a particular user preference.
Algorithmic art or algorithm art is art, mostly visual art, in which the design is generated by an algorithm. Algorithmic artists are sometimes called algorists.
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.
ECJ is a freeware evolutionary computation research system written in Java. It is a framework that supports a variety of evolutionary computation techniques, such as genetic algorithms, genetic programming, evolution strategies, coevolution, particle swarm optimization, and differential evolution. The framework models iterative evolutionary processes using a series of pipelines arranged to connect one or more subpopulations of individuals with selection, breeding (such as crossover, and mutation operators that produce new individuals. The framework is open source and is distributed under the Academic Free License. ECJ was created by Sean Luke, a computer science professor at George Mason University, and is maintained by Sean Luke and a variety of contributors.
Generative design (GD) is an iterative design process that generates outputs that meet specified constraints to varying degrees. In a second phase, designers can then provide feedback to the generator that explores the feasible region by selecting preferred outputs or changing input parameters for future iterations. Either or both phases can be done by humans or software. One method is to use a generative adversarial network, which is a pair of neural networks. The first generates a trial output. The second provides feedback for the next iteration.
Design Automation usually refers to electronic design automation, or Design Automation which is a Product Configurator. Extending Computer-Aided Design (CAD), automated design and Computer-Automated Design (CAutoD) are more concerned with a broader range of applications, such as automotive engineering, civil engineering, composite material design, control engineering, dynamic system identification and optimization, financial systems, industrial equipment, mechatronic systems, steel construction, structural optimisation, and the invention of novel systems.
Evolutionary biology, in particular the understanding of how organisms evolve through natural selection, is an area of science with many practical applications. Creationists often claim that the theory of evolution lacks any practical applications; however, this claim has been refuted by scientists.
Stephanie Forrest is an American computer scientist and director of the Biodesign Center for Biocomputing, Security and Society at the Biodesign Institute at Arizona State University. She was previously Distinguished Professor of Computer Science at the University of New Mexico in Albuquerque. She is best known for her work in adaptive systems, including genetic algorithms, computational immunology, biological modeling, automated software repair, and computer security.
Multi Expression Programming (MEP) is an evolutionary algorithm for generating mathematical functions describing a given set of data. MEP is a Genetic Programming variant encoding multiple solutions in the same chromosome. MEP representation is not specific. In the simplest variant, MEP chromosomes are linear strings of instructions. This representation was inspired by Three-address code. MEP strength consists in the ability to encode multiple solutions, of a problem, in the same chromosome. In this way, one can explore larger zones of the search space. For most of the problems this advantage comes with no running-time penalty compared with genetic programming variants encoding a single solution in a chromosome.
The Genetic and Evolutionary Computation Conference (GECCO) is the premier conference in the area of genetic and evolutionary computation. GECCO has been held every year since 1999, when it was first established as a recombination of the International Conference on Genetic Algorithms (ICGA) and the Annual Genetic Programming Conference (GP).
The following outline is provided as an overview of and topical guide to machine learning:
John Francis Dunn was an American music and art software developer. He created several visual art, music, and design software programs, including Lumena, MusicBox, SoftStep, and others. He has also written and performed a variety of electronic music compositions throughout his career. He was a graduate of Sonia Landy Sheridan's Generative Systems program at the School of the Art Institute of Chicago. He also founded Time Arts, Inc. and Algorithmic Arts.
