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, 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.
General Purpose Simulation System (GPSS) is a discrete time simulation general-purpose programming language, where a simulation clock advances in discrete steps. A system is modelled as transactions enter the system and are passed from one service to another. It is used primarily as a process flow oriented simulation language; this is particularly well-suited for problems such as a factory.
Karl Sims is a computer graphics artist and researcher, who is best known for using particle systems and artificial life in computer animation.
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.
Evolutionary robotics is an embodied approach to Artificial Intelligence (AI) in which robots are automatically designed using Darwinian principles of natural selection. The design of a robot, or a subsystem of a robot such as a neural controller, is optimized against a behavioral goal. Usually, designs are evaluated in simulations as fabricating thousands or millions of designs and testing them in the real world is prohibitively expensive in terms of time, money, and safety.
In computer programming, gene expression programming (GEP) is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism. And like living organisms, the computer programs of GEP are also encoded in simple linear chromosomes of fixed length. Thus, GEP is a genotype–phenotype system, benefiting from a simple genome to keep and transmit the genetic information and a complex phenotype to explore the environment and adapt to it.
In artificial intelligence, an intelligent agent (IA) is an agent acting in an intelligent manner; It perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome.
Coalescent theory is a model of how alleles sampled from a population may have originated from a common ancestor. In the simplest case, coalescent theory assumes no recombination, no natural selection, and no gene flow or population structure, meaning that each variant is equally likely to have been passed from one generation to the next. The model looks backward in time, merging alleles into a single ancestral copy according to a random process in coalescence events. Under this model, the expected time between successive coalescence events increases almost exponentially back in time. Variance in the model comes from both the random passing of alleles from one generation to the next, and the random occurrence of mutations in these alleles.
Life simulation games form a subgenre of simulation video games in which the player lives or controls one or more virtual characters. Such a game can revolve around "individuals and relationships, or it could be a simulation of an ecosystem". Other terms include artificial life game and simulated life game (SLG).
TechnoSphere was an online digital environment launched on September 1, 1995 and hosted on a computer at a UK university. Created by Jane Prophet and Dr. Gordon Selley, TechnoSphere was a place where users from around the globe could create creatures and release them into the 3D environment, described by the creators as a "digital ecology." Earlier incarnations of TechnoSphere did not have the advantage of web-accessible 3D graphics, but was still governed by chaos theory and similar algorithms that determined each creature's unique behavior based on their components and interactions with each other and their environment.
Evolutionary music is the audio counterpart to evolutionary art, whereby algorithmic music is created using an evolutionary algorithm. The process begins with a population of individuals which by some means or other produce audio, which is either initialized randomly or based on human-generated music. Then through the repeated application of computational steps analogous to biological selection, recombination and mutation the aim is for the produced audio to become more musical. Evolutionary sound synthesis is a related technique for generating sounds or synthesizer instruments. Evolutionary music is typically generated using an interactive evolutionary algorithm where the fitness function is the user or audience, as it is difficult to capture the aesthetic qualities of music computationally. However, research into automated measures of musical quality is also active. Evolutionary computation techniques have also been applied to harmonization and accompaniment tasks. The most commonly used evolutionary computation techniques are genetic algorithms and genetic programming.
Neuron is a simulation environment for modeling individual and networks of neurons. It was primarily developed by Michael Hines, John W. Moore, and Ted Carnevale at Yale and Duke.
Evolution in Variable Environment (EVE) is a computer program designed to simulate microbial cellular behavior in various environments. The prediction of cellular responses is a rapidly evolving topic in systems biology and computational biology. The goal is to predict the behavior a particular organism in response to a set of environmental stimuli in silico. Such predictions can have a significant impact on preventive medicine, biotechnology, and microbe re-engineering. Computational prediction of behavior has two major components: the integration and simulation of vast biological networks and the creation of external stimuli. Current limitations of the method are: lack of comprehensive experimental data on the various cellular subsystems and inadequate computational algorithms.
Wagner's gene network model is a computational model of artificial gene networks, which explicitly modeled the developmental and evolutionary process of genetic regulatory networks. A population with multiple organisms can be created and evolved from generation to generation. It was first developed by Andreas Wagner in 1996 and has been investigated by other groups to study the evolution of gene networks, gene expression, robustness, plasticity and epistasis.
Artificial life is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.
EcoSim is an individual-based predator-prey ecosystem simulation in which agents can evolve. It has been designed to investigate several broad ecological questions, as well as long-term evolutionary patterns and processes such as speciation and macroevolution. EcoSim has been designed by Robin Gras at the University of Windsor in 2009 and it is still currently used for research in his Bioinformatics and Ecosystem Simulation Lab.
Gene Pool is an artificial life simulation created by Jeffrey Ventrella in 1997. It features physics-based 2D proto-swimming creatures (swimbots) that compete for mates and food.
