In the field of artificial intelligence (AI), tasks that are hypothesized to require artificial general intelligence to solve are informally known as AI-complete or AI-hard. Calling a problem AI-complete reflects the belief that it cannot be solved by a simple specific algorithm.
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, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, 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 backpropagation with a fixed topology.
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems. Their use is always of interest when exact or other (approximate) methods are not available or are not expedient, either because the calculation time is too long or because, for example, the solution provided is too imprecise.
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 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.
Robert Anthony Kowalski is an American-British logician and computer scientist, whose research is concerned with developing both human-oriented models of computing and computational models of human thinking. He has spent most of his career in the United Kingdom.
David B. Fogel is a pioneer in evolutionary computation.
A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.
Zbigniew Michalewicz is an entrepreneur, author and professor in the fields of mathematical optimisation and new technologies. He is the author of over 250 articles and 25 books which have been widely cited. He is the co-founder of NuTech Solutions, SolveIT Software, and Complexica where he currently serves as the Chief Scientific Officer.
Yaochu Jin was born in Wujiang, Jiangsu Province, China in 1966. He received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001.
Gary Bryce Fogel is an American biologist and computer scientist. He is the Chief Executive Officer of Natural Selection, Inc. He is most known for his applications of computational intelligence and machine learning to bioinformatics, computational biology, and industrial optimization.
Amir Hussain is a cognitive scientist, the director of Cognitive Big Data and Cybersecurity (CogBID) Research Lab at Edinburgh Napier University He is a professor of computing science. He is founding Editor-in-Chief of Springer Nature's internationally leading Cognitive Computation journal and the new Big Data Analytics journal. He is founding Editor-in-Chief for two Springer Book Series: Socio-Affective Computing and Cognitive Computation Trends, and also serves on the Editorial Board of a number of other world-leading journals including, as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics (Systems) and the IEEE Computational Intelligence Magazine.
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.
Weng Cho Chew is a Malaysian-American electrical engineer and applied physicist known for contributions to wave physics, especially computational electromagnetics. He is a Distinguished Professor of Electrical and Computer Engineering at Purdue University.
David G. Stork is a scientist and author, who has made contributions to machine learning, pattern recognition, computer vision, artificial intelligence, computational optics, image analysis of fine art, and related fields.
Michael Bronstein is an Israeli computer scientist and entrepreneur. He is a computer science professor at the University of Oxford and scientific director of Aithyra Institute at the Vienna Biocenter in Austria.
Chan-Jin Chung, commonly known as CJ Chung, is a full professor of computer science at Lawrence Technological University (LTU) in Michigan, USA. He founded an international autonomous robotics competition called Robofest in the 1999–2000 academic year as well as numerous educational programs for youth by integrating STEM, arts, autonomous robotics, and computer science. He also served as the founding USA National Organizer of World Robot Olympiad (WRO) in 2014 and 2015. He also started the WISER conference in 2014. He is working on developing a computer science curriculum for connected and autonomous vehicles (CAV) with a support from National Science Foundation . His research areas include evolutionary computation, cultural algorithms, intelligent systems & autonomous mobile robotics, software engineering, machine learning & deep learning, computer science education, and educational robotics.
This is a chronological table of metaheuristic algorithms that only contains fundamental computational intelligence algorithms. Hybrid algorithms and multi-objective algorithms are not listed in the table below.
Soft computing is an umbrella term used to describe types of algorithms that produce approximate solutions to unsolvable high-level problems in computer science. Typically, traditional hard-computing algorithms heavily rely on concrete data and mathematical models to produce solutions to problems. Soft computing was coined in the late 20th century. During this period, revolutionary research in three fields greatly impacted soft computing. Fuzzy logic is a computational paradigm that entertains the uncertainties in data by using levels of truth rather than rigid 0s and 1s in binary. Next, neural networks which are computational models influenced by human brain functions. Finally, evolutionary computation is a term to describe groups of algorithm that mimic natural processes such as evolution and natural selection.
