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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.
Evolutionary art is a branch of generative art, in which the artist does not do the work of constructing the artwork, but rather lets a system do the construction. In evolutionary art, initially generated art is put through an iterated process of selection and modification to arrive at a final product, where it is the artist who is the selective agent.
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.
Tierra is a computer simulation developed by ecologist Thomas S. Ray in the early 1990s in which computer programs compete for time and space. In this context, the computer programs in Tierra are considered to be evolvable and can mutate, self-replicate and recombine. Tierra's virtual machine is written in C. It operates on a custom instruction set designed to facilitate code changes and reordering, including features such as jump to template.
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.
Luc Steels is a Belgian scientist and artist. Steels is considered a pioneer of Artificial Intelligence in Europe who has made contributions to expert systems, behavior-based robotics, artificial life and evolutionary computational linguistics. He was a fellow of the Catalan Institution for Research and Advanced Studies ICREA associated as a research professor with the Institute for Evolutionary Biology (UPF/CSIC) in Barcelona. He was formerly founding Director of the Artificial Intelligence Laboratory of the Vrije Universiteit Brussel and founding director of the Sony Computer Science Laboratory in Paris. Steels has also been active in the arts collaborating with visual artists and theater makers and composing music for opera.
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.
In artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory for use in problem-solving.
The learnable evolution model (LEM) is a non-Darwinian methodology for evolutionary computation that employs machine learning to guide the generation of new individuals. Unlike standard, Darwinian-type evolutionary computation methods that use random or semi-random operators for generating new individuals, LEM employs hypothesis generation and instantiation operators.
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.
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 considered a sub-field of evolutionary computation, although the principles of artificial development have also been used within stand-alone computational models.
Hypercube-based NEAT, or HyperNEAT, is a generative encoding that evolves artificial neural networks (ANNs) with the principles of the widely used NeuroEvolution of Augmented Topologies (NEAT) algorithm developed by Kenneth Stanley. It is a novel technique for evolving large-scale neural networks using the geometric regularities of the task domain. It uses Compositional Pattern Producing Networks (CPPNs), which are used to generate the images for Picbreeder.orgArchived 2011-07-25 at the Wayback Machine and shapes for EndlessForms.comArchived 2018-11-14 at the Wayback Machine. HyperNEAT has recently been extended to also evolve plastic ANNs and to evolve the location of every neuron in the network.
Digital morphogenesis is a type of generative art in which complex shape development, or morphogenesis, is enabled by computation. This concept is applicable in many areas of design, art, architecture, and modeling. The concept was originally developed in the field of biology, later in geology, geomorphology, and architecture.
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.
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.
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.
Luis M. Rocha is the George J. Klir Professor of Systems Science at the Thomas J. Watson College of Engineering and Applied Science, Binghamton University. He has been director of the NSF-NRT Complex Networks and Systems graduate Program in Informatics at Indiana University, Bloomington, USA. He is also director of the Center for Social and Biomedical Complexity, between Binghamton University and Indiana University, Bloomington, a Fulbright Scholar, and Principal Investigator at the Instituto Gulbenkian de Ciencia, Portugal. His research is on complex systems and networks, computational and systems biology, biomedical complexity and digital health, and computational intelligence.
Werner Dilger was a German professor of computer science and held the chair for artificial intelligences at the Chemnitz. His last major area of research was the study of artificial immune systems.
Professor Emma Hart, FRSE is an English computer scientist known for her work in artificial immune systems (AIS), evolutionary computation and optimisation. She is a professor of computational intelligence at Edinburgh Napier University, editor-in-chief of the Journal of Evolutionary Computation, and D. Coordinator of the Future & Emerging Technologies (FET) Proactive Initiative, Fundamentals of Collective Adaptive Systems.
Lee Altenberg is an American theoretical biologist. He is on the faculty of the Departments of Information and Computer Sciences and of Mathematics at the University of Hawaiʻi at Mānoa. He is best known for his work that helped establish the evolution of evolvability and modularity in the genotype–phenotype map as areas of investigation in evolutionary biology, for moving theoretical concepts between the fields of evolutionary biology and evolutionary computation, and for his mathematical unification and generalization of modifier gene models for the evolution of biological information transmission, putting under a single mathematical framework the evolution of mutation rates, recombination rates, sexual reproduction rates, and dispersal rates.
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