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In artificial intelligence, 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.
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks developed by Ken Stanley in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying").
Jürgen Schmidhuber is a computer scientist most noted for his work in the field of artificial intelligence, deep learning and artificial neural networks. He is a co-director of the Dalle Molle Institute for Artificial Intelligence Research in Manno, in the district of Lugano, in Ticino in southern Switzerland. He is sometimes called the "father of (modern) AI" or, one time, the "father of deep learning."
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, topology and rules. It is most commonly applied in artificial life, general game playing and evolutionary robotics. The main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use gradient descent on a neural network with a fixed topology.
Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
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 computer science and operations research, a memetic algorithm (MA) is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence.
General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully. For many games like chess, computers are programmed to play these games using a specially designed algorithm, which cannot be transferred to another context. For example, a chess-playing computer program cannot play checkers. A general game playing system, if well designed, would be able to help in other areas, such as in providing intelligence for search and rescue missions.
Dr. David B. Fogel, is a pioneer in evolutionary computation. Dr. Fogel received his Ph.D. in engineering from the University of California, San Diego in 1992. He is currently Chief Scientist at Trials.ai, and holds other founding positions at Natural Selection, Inc., Color Butler, Inc., and Effect Technologies, Inc., the maker of the patented EffectCheck sentiment analysis software tool. He advises several AI companies in the areas of B2B lead generation, logistics, and employee retention, as well as other areas.
Dr. Lawrence Jerome Fogel was a pioneer in evolutionary computation and human factors analysis. He is known as the inventor of active noise cancellation and the father of evolutionary programming. His scientific career spanned nearly six decades and included electrical engineering, aerospace engineering, communication theory, human factors research, information processing, cybernetics, biotechnology, artificial intelligence, and computer science.
IEEE Transactions on Evolutionary Computation is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. The editor-in-chief is Kay Chen Tan. According to the Journal Citation Reports, the journal has a 2017 impact factor of 8.124.
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. 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.org and shapes for EndlessForms.com. HyperNEAT has recently been extended to also evolve plastic ANNs and to evolve the location of every neuron in the network.
The IEEE Computational Intelligence Society is a professional society of the Institute of Electrical and Electronics Engineers (IEEE) focussing on "the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing neural networks, connectionist systems, genetic algorithms, evolutionary programming, fuzzy systems, and hybrid intelligent systems in which these paradigms are contained".
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. They have applications in image and video recognition, recommender systems, image classification, medical image analysis, and natural language processing.
In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in game play. MCTS was introduced in 2006 for computer Go. It has been used in other board games like chess and shogi, games with incomplete information such as bridge and poker, as well as in turn-based-strategy video games.
This glossary of artificial intelligence terms is about artificial intelligence, its sub-disciplines, and related fields.
AlexNet is the name of a convolutional neural network, designed by Alex Krizhevsky, and published with Ilya Sutskever and Krizhevsky's PhD advisor Geoffrey Hinton, who was originally resistant to the idea of his student.
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Further reading
- Chellapilla, Kumar; Fogel, David B. (September 1999). "Evolution, Neural Networks, Games, and Intelligence" (PDF). Proceedings of the IEEE . 87 (9): 1471–1496. CiteSeerX 10.1.1.99.979 . doi:10.1109/5.784222. Archived from the original (PDF) on 2011-05-30.
- Chellapilla, Kumar; Fogel, David B. (1999). "Evolving Neural Networks to Play Checkers without Expert Knowledge" (PDF). IEEE Transactions on Neural Networks . 10 (6): 1382–1391. doi:10.1109/72.809083. PMID 18252639. Archived from the original (PDF) on 2011-05-30.
- Chellapilla, Kumar; Fogel, David B. (2001). "Evolving an Expert Checkers Playing Program without Using Human Expertise" (PDF). IEEE Transactions on Evolutionary Computation . 5 (4): 422–428. doi:10.1109/4235.942536. Archived from the original (PDF) on 2011-05-30.
- Fogel, David B. (2002). Blondie24: Playing at the Edge of AI. San Francisco, CA.: Morgan Kaufmann Publishers. ISBN 1-55860-783-8.