Computational intelligence

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In computer science, computational intelligence (CI) refers to concepts, paradigms, algorithms and implementations of systems that are designed to show "intelligent" behavior in complex and changing environments. [1] These systems are aimed at mastering complex tasks in a wide variety of technical or commercial areas and offer solutions that recognize and interpret patterns, control processes, support decision-making or autonomously manoeuvre vehicles or robots in unknown environments, among other things. [2] These concepts and paradigms are characterized by the ability to learn or adapt to new situations, to generalize, to abstract, to discover and to associate. [3] Nature-analog or at least nature-inspired methods play a key role in this. [1]

Contents

CI approaches primarily address those complex real-world problems for which mathematical or traditional modeling is not appropriate for various reasons: the processes cannot be described exactly with complete knowledge, the processes are too complex for mathematical reasoning, they contain some uncertainties during the process, such as unforeseen changes in the environment or in the process itself, or the processes are simply stochastic in nature. Thus, CI techniques are properly aimed at processes that are ill-defined, complex, nonlinear, time-varying and/or stochastic. [4]

A recent definition of the IEEE Computational Intelligence Societey describes CI as the theory, design, application and development of biologically and linguistically motivated computational paradigms. Traditionally the three main pillars of CI have been Neural Networks, Fuzzy Systems and Evolutionary Computation. ... CI is an evolving field and at present in addition to the three main constituents, it encompasses computing paradigms like ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. ... Over the last few years there has been an explosion of research on Deep Learning, in particular deep convolutional neural networks. Nowadays, deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems are based on CI. [5] However, as CI is an emerging and developing field there is no final definition of CI, [6] [7] [8] especially in terms of the list of concepts and paradigms that belong to it. [3] [9] [10]

The general requirements for the development of an “intelligent system” are ultimately always the same, namely the simulation of intelligent thinking and action in a specific area of application. To do this, the knowledge about this area must be represented in a model so that it can be processed. The quality of the resulting system depends largely on how well the model was chosen in the development process. Sometimes data-driven methods are suitable for finding a good model and sometimes logic-based knowledge representations deliver better results. Hybrid models are usually used in real applications. [2]

According to actual textbooks, the following methods and paradigms, which largely complement each other, can be regarded as parts of CI: [11] [12] [13] [14] [15] [16] [17]

Relationship between hard and soft computing and artificial and computational intelligence

Artificial intelligence (AI) is used in the media, but also by some of the scientists involved, as a kind of umbrella term for the various techniques associated with it or with CI [5] [18] . Craenen and Eiben state that attempts to define or at least describe CI can usually be assigned to one or more of the following groups:

Relationship between hard computing and artificial intelligence on the one hand and soft computing and computational intelligence on the other. Relationship AI-HC CI-SC.svg
Relationship between hard computing and artificial intelligence on the one hand and soft computing and computational intelligence on the other.

The relationship between CI and AI has been a frequently discussed topic during the development of CI. While the above list implies that they are synonyms, the vast majority of AI/CI researchers working on the subject consider them to be distinct fields, where either [8] [18]

The view of the first of the above three points goes back to Zadeh, the founder of the fuzzy set theory, who differentiated machine intelligence into hard and soft computing techniques, which are used in artificial intelligence on the one hand and computational intelligence on the other. [19] [20] In hard computing (HC) and AI, inaccuracy and uncertainty are undesirable characteristics of a system, while soft computing (SC) and thus CI focus on dealing with these characteristics. [14] The adjacent figure illustrates these relationships and lists the most important CI techniques. [6] Another frequently mentioned distinguishing feature is the representation of information in symbolic form in AI and in sub-symbolic form in CI techniques. [17] [21]

Hard computing is a conventional computing method based on the principles of certainty and accuracy and it is deterministic. It requires a precisely stated analytical model of the task to be processed and a prewritten program, i.e. a fixed set of instructions. The models used are based on Boolean logic (also called crisp logic [22] ), where e.g. an element can be either a member of a set or not and there is nothing in between. When applied to real-world tasks, systems based on HC result in specific control actions defined by a mathematical model or algorithm. If an unforeseen situation occurs that is not included in the model or algorithm used, the action will most likely fail. [23] [24] [25] [26]

Soft computing, on the other hand, is based on the fact that the human mind is capable of storing information and processing it in a goal-oriented way, even if it is imprecise and lacks certainty. [20] SC is based on the model of the human brain with probabilistic thinking, fuzzy logic and multi-valued logic. Soft computing can process a wealth of data and perform a large number of computations, which may not be exact, in parallel. For hard problems for which no satisfying exact solutions based on HC are available, SC methods can be applied successfully. SC methods are usually stochastic in nature i.e., they are a randomly defined processes that can be analyzed statistically but not with precision. Up to now, the results of some CI methods, such as deep learning, cannot be verified and it is also not clear what they are based on. This problem represents an important scientific issue for the future. [23] [24] [25] [26]

AI and CI are catchy terms, [18] but they are also so similar that they can be confused. The meaning of both terms has developed and changed over a long period of time, [27] [28] with AI being used first. [3] [9] Bezdek describes this impressively and concludes that such buzzwords are frequently used and hyped by the scientific community, science management and (science) journalism. [18] Not least because AI and biological intelligence are emotionally charged terms [3] [18] and it is still difficult to find a generally accepted definition for the basic term intelligence. [3] [10]

History

In 1950, Alan Turing, one of the founding fathers of computer science, developed a test for computer intelligence known as the Turing test . [29] In this test, a person can ask questions via a keyboard and a monitor without knowing whether his counterpart is a human or a computer. A computer is considered intelligent if the interrogator cannot distinguish the computer from a human. This illustrates the discussion about intelligent computers at the beginning of the computer age.

The term Computational Intelligence was first used as the title of the journal of the same name in 1985 [30] [31] and later by the IEEE Neural Networks Council (NNC), which was founded 1989 by a group of researchers interested in the development of biological and artificial neural networks. [32] On November 21, 2001, the NNC became the IEEE Neural Networks Society, to become the IEEE Computational Intelligence Society two years later by including new areas of interest such as fuzzy systems and evolutionary computation.

The NNC helped organize the first IEEE World Congress on Computational Intelligence in Orlando, Florida in 1994. [32] On this conference the first clear definition of Computational Intelligence was introduced by Bezdek: A system is computationally intelligent when it: deals with only numerical (low-level) data, has pattern-recognition components, does not use knowledge in the AI sense; and additionally when it (begins to) exhibit (1) computational adaptivity; (2) computational fault tolerance; (3) speed approaching human-like turnaround and (4) error rates that approximate human performance. [33]

Today, with machine learning and deep learning in particular utilizing a breadth of supervised, unsupervised, and reinforcement learning approaches, the CI landscape has been greatly enhanced, with novell intelligent approaches.

The five main algorithmic approaches of CI and their applications

The main applications of Computational Intelligence include computer science, engineering, data analysis and bio-medicine.

Fuzzy logic

Unlike conventional Boolean logic, fuzzy logic is based on fuzzy sets. In both models, a property of an object is defined as belonging to a set; in fuzzy logic, however, the membership is not sharply defined by a yes/no distinction, but is graded gradually. This is done using membership functions that assign a real number between 0 and 1 to each element as the degree of membership. The new set operations introduced in this way define the operations of an associated logic calculus that allows the modeling of inference processes, i.e. logical reasoning. [34] Therefore, fuzzy logic is well suited for engineering decisions without clear certainties and uncertainties or with imprecise data - as with natural language-processing technologies [35] but it doesn't have learning abilities. [36]

This technique tends to apply to a wide range of domains such as control engineering, [37] image processing, [38] fuzzy data clustering [38] [39] and decision making. [35] Fuzzy logic-based control systems can be found, for example, in the field of household appliances in washing machines, dish washers, microwave ovens, etc. or in the area of motor vehicles in gear transmission and braking systems. This principle can also be encountered when using a video camera, as it helps to stabilize the image when the camera is held unsteadily. Other areas such as medical diagnostics, satellite controllers and business strategy selection are just a few more examples of today's application of fuzzy logic. [35] [40]

Neural networks

An important field of CI is the development of artificial neural networks (ANN) based on the biological ones, which can be defined by three main components: the cell-body which processes the information, the axon, which is a device enabling the signal conducting, and the synapse, which controls signals. [41] [42] Therefore, ANNs are very well suited for distributed information processing systems, enabling the process and the learning from experiential data. [43] [44] ANNs aim to mimic cognitive processes of the human brain. The main advantages of this technology therefore include fault tolerance, pattern recognition even with noisy images and the ability to learn. [41] [44]

Concerning its applications, neural networks can be classified into five groups: data analysis and classification, associative memory, data clustering or compression, generation of patterns, and control systems. [45] [43] [41] The numerous applications include, for example, the analysis and classification of medical data, including the creation of diagnoses, speech recognition, data mining, image processing, forecasting, robot control, credit approval, pattern recognition, face and fraud detection and dealing with nonlinearities of a system in order to control it. [41] [43] [45] ANNs have the latter area of application and data clustering in common with fuzzy logic. Generative systems based on deep learning and convolutional neural networks, such as chatGPT or DeepL, are a relatively new field of application.

Evolutionary computation

Evolutionary computation can be seen as a family of methods and algorithms for global optimization, which are usually based on a population of candidate solutions. They are inspired by biological evolution and are often summarized as evolutionary algorithms. [46] These include the genetic algorithms, evolution strategy, genetic programming and many others. [47] They are considered as problem solvers for tasks not solvable by traditional mathematical methods [48] and are frequently used for optimization including multi-objective optimization. [49]

Learning theory

Still looking for a way of "reasoning" close to the humans' one, learning theory is one of the main approaches of CI. In psychology, learning is the process of bringing together cognitive, emotional and environmental effects and experiences to acquire, enhance or change knowledge, skills, values and world views (Ormrod, 1995; Illeris, 2004). [50] Learning theories then helps understanding how these effects and experiences are processed, and then helps making predictions based on previous experience. [51]

Probabilistic methods

Being one of the main elements of fuzzy logic, probabilistic methods firstly introduced by Paul Erdos and Joel Spencer (1974), [52] aim to evaluate the outcomes of a Computation Intelligent system, mostly defined by randomness. [53] Therefore, probabilistic methods bring out the possible solutions to a problem, based on prior knowledge.

Impact on university education

According to bibliometrics studies, computational intelligence plays a key role in research. [54] All the major academic publishers are accepting manuscripts in which a combination of Fuzzy logic, neural networks and evolutionary computation is discussed. On the other hand, Computational intelligence isn't available in the university curriculum. [55] The amount of technical universities in which students can attend a course is limited. Only British Columbia, Technical University of Dortmund (involved in the European fuzzy boom) and Georgia Southern University are offering courses from this domain.

The reason why major university are ignoring the topic is because they don't have the resources. The existing computer science courses are so complex, that at the end of the semester there is no room for fuzzy logic. [56] Sometimes it is taught as a subproject in existing introduction courses, but in most cases the universities are preferring courses about classical AI concepts based on Boolean logic, turing machines and toy problems like blocks world.

Since a while with the upraising of STEM education, the situation has changed a bit. [57] There are some efforts available in which multidisciplinary approaches are preferred which allows the student to understand complex adaptive systems. [58] These objectives are discussed only on a theoretical basis. The curriculum of real universities wasn't adapted yet.

Publications

See also

Notes

References

  1. 1 2 Kruse, Rudolf; Mostaghim, Sanaz; Borgelt, Christian; Braune, Christian; Steinbrecher, Matthias (2022). "Preface". Computational Intelligence: A Methodological Introduction. Texts in Computer Science (3rd ed.). Cham: Springer International Publishing. pp. V. doi:10.1007/978-3-030-42227-1. ISBN   978-3-030-42226-4.
  2. 1 2 Kruse, Rudolf; Mostaghim, Sanaz; Borgelt, Christian (2022). "Intelligent Systems". Computational Intelligence: A Methodological Introduction. Texts in Computer Science (3rd ed.). Cham: Springer International Publishing. pp. 1–2. doi:10.1007/978-3-030-42227-1. ISBN   978-3-030-42226-4.
  3. 1 2 3 4 5 Engelbrecht, Andries P. (2007). "Introduction to Computational Intelligence". Computational Intelligence: An Introduction (2nd ed.). Chichester, England ; Hoboken, NJ: John Wiley & Sons. p. 3-4. ISBN   978-0-470-03561-0. OCLC   133465571.
  4. ↑ Siddique, N. H.; Adeli, Hojjat (2013). "Computational Intelligence". Computational intelligence: synergies of fuzzy logic, neural networks, and evolutionary computing. Chichester, West Sussex, United Kingdom: John Wiley & Sons Inc. pp. 1–2. ISBN   978-1-118-53481-6.
  5. 1 2 "What is Computational Intelligence?". IEEE Computational Intelligence Society. Retrieved January 18, 2025.
  6. 1 2 3 Siddique, Nazmul; Adeli, Hojjat (2013). "Paradigms of Computational Intelligence". Computational intelligence: synergies of fuzzy logic, neural networks, and evolutionary computing. Chichester, West Sussex, United Kingdom: John Wiley & Sons Inc. pp. 2–3. ISBN   978-1-118-33784-4.
  7. ↑ Bezdek, James C. (April 2016). "(Computational) Intelligence: What's in a Name?". IEEE Systems, Man, and Cybernetics Magazine. 2 (2): 11. doi:10.1109/MSMC.2016.2558778. ISSN   2333-942X.
  8. 1 2 3 Craenen, Bart; Eiben, A.E. (2009). "Computational Intelligence". In Joost, N.K. (ed.). Encyclopedia of Life Support Systems (EOLSS), vol. 4, Artificial Intelligence. Oxford, UK: Eolss Publishers. Developed under the Auspices of the UNESCO.
  9. 1 2 Duch, Włodzisław (2007). "What Is Computational Intelligence and Where Is It Going?". In Duch, Włodzisław; Mańdziuk, Jacek (eds.). Challenges for Computational Intelligence. Studies in Computational Intelligence. Vol. 63. Berlin, Heidelberg: Springer. pp. 1–13. doi:10.1007/978-3-540-71984-7. ISBN   978-3-540-71983-0.
  10. 1 2 Fulcher, John (2008). "Introduction, Overview, Definitions". In Fulcher, John; Jain, L.C. (eds.). Computational Intelligence: A Compendium. Studies in Computational Intelligence. Vol. 115. Berlin, Heidelberg: Springer. pp. 3–7. doi:10.1007/978-3-540-78293-3. ISBN   978-3-540-78292-6.
  11. 1 2 3 4 5 6 Engelbrecht, Andries P. (2002). "Computational Intelligence Paradigms". Computational intelligence: an introduction (2nd ed.). Chichester, England ; Hoboken, N.J: J. Wiley & Sons. pp. 4–11. ISBN   978-0-470-03561-0. OCLC   133465571.
  12. 1 2 3 4 5 6 7 Siddique, Nazmul; Adeli, Hojjat (2013). "Approaches to Computational Intelligence". Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks, and Evolutionary Computing. Chichester, West Sussex, United Kingdom: John Wiley & Sons Inc. pp. 3–10. ISBN   978-1-118-33784-4.
  13. 1 2 3 4 5 6 Kruse, Rudolf; Mostaghim, Sanaz; Borgelt, Christian; Braune, Christian; Steinbrecher, Matthias (2022). "Computational Intelligence". Computational Intelligence: A Methodological Introduction. Texts in Computer Science (3rd ed.). Cham: Springer International Publishing. pp. 2–3. doi:10.1007/978-3-030-42227-1. ISBN   978-3-030-42226-4.
  14. 1 2 3 4 5 6 Eberhart, Russell C.; Shi, Yuhui (2007). "Preface". Computational Intelligence: Concepts to Implementations. Amsterdam, Boston: Elsevier/Morgan Kaufmann Publishers. pp. XIII–XIX. ISBN   978-1-55860-759-0.
  15. 1 2 3 4 5 6 Hanne, Thomas; Dornberger, Rolf (2017). "Computational Intelligence". Computational Intelligence in Logistics and Supply Chain Management. International series in operations research & management science. Springer-Verlag. pp. 13–41. doi:10.1007/978-3-319-40722-7. ISBN   978-3-319-40722-7.
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  17. 1 2 3 4 5 6 7 8 Hošovský, Alexander; Piteľ, Ján; Trojanová, Monika; Židek, Kamil (2021), Matt, Dominik T.; Modrák, Vladimír; Zsifkovits, Helmut (eds.), "Computational Intelligence in the Context of Industry 4.0", Implementing Industry 4.0 in SMEs, Cham: Springer International Publishing, pp. 30–31, doi:10.1007/978-3-030-70516-9_2, ISBN   978-3-030-70515-2
  18. 1 2 3 4 5 Bezdek, James C. (April 2016). "(Computational) Intelligence: What's in a Name?" (PDF). IEEE Systems, Man, and Cybernetics Magazine. 2 (2): 4–14. doi:10.1109/MSMC.2016.2558778. ISSN   2333-942X.
  19. ↑ Zadeh, Lotfi A. (April 1994). "Fuzzy Logic, Neural Networks, and Soft Computing". Communications of the ACM. 37 (3): 77–84. doi:10.1145/175247.175255. ISSN   0001-0782.
  20. 1 2 Zadeh, Lotfi A. (1998), Kaynak, Okyay; Zadeh, Lotfi A.; Türkşen, Burhan; Rudas, Imre J. (eds.), "Roles of Soft Computing and Fuzzy Logic in the Conception, Design and Deployment of Information/Intelligent Systems", Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Berlin, Heidelberg: Springer, pp. 1–9, doi:10.1007/978-3-642-58930-0_1, ISBN   978-3-642-63796-4
  21. ↑ Kruse, Rudolf; Mostaghim, Sanaz; Borgelt, Christian; Braune, Christian; Steinbrecher, Matthias; Klawonn, Frank; Moewes, Christian (2022). "Introduction to Artificial Neural Networks". Computational Intelligence: A Methodological Introduction. Texts in Computer Science (3rd ed.). Cham, Switzerland: Springer. p. 8. ISBN   978-3-030-42226-4.
  22. ↑ "Fuzzy Sets and Pattern Recognition". www.cs.princeton.edu. Retrieved November 5, 2015.
  23. 1 2 "Soft Computing vs. Hard Computing: Key Differences". WisdomPlexus. December 26, 2024. Retrieved February 7, 2025.
  24. 1 2 Sidda, Sakunthala; Kiranmayi, R.; Nagaraju Mandadi, P. (February 28, 2018). "Soft Computing Techniques and Applications in Electrical Drives Fuzzy logic, and Genetic Algorithm". HELIX. 8 (2): 3285–3289. doi:10.29042/2018-3285-3289. S2CID   57747778.
  25. 1 2 "Soft Computing vs. Hard Computing". University of the People. June 19, 2024. Retrieved February 7, 2025.
  26. 1 2 "Soft Computing vs. Hard Computing: Understanding the Differences and Applications". CINET - Computational Intelligence Mastery. October 11, 2022. Retrieved February 8, 2025.
  27. ↑ Bezdek, James C. (1998), Kaynak, Okyay; Zadeh, Lotfi A.; Türkşen, Burhan; Rudas, Imre J. (eds.), "Computational Intelligence Defined - By Everyone !", Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Berlin, Heidelberg: Springer, pp. 10–37, doi:10.1007/978-3-642-58930-0_2, ISBN   978-3-642-63796-4 , retrieved February 3, 2025
  28. ↑ Engelbrecht, Andries P. (2007). "Short History". Computational Intelligence: An Introduction (2nd ed.). Chichester, England ; Hoboken, NJ: John Wiley & Sons. p. 11-13. ISBN   978-0-470-03561-0. OCLC   133465571.
  29. ↑ Turing, Alan M. (October 1, 1950). "I.—COMPUTING MACHINERY AND INTELLIGENCE". Mind. LIX (236): 433–460. doi:10.1093/mind/LIX.236.433. ISSN   1460-2113.
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  31. ↑ Bezdek, James C. (1998), Kaynak, Okyay; Zadeh, Lotfi A.; Türkşen, Burhan; Rudas, Imre J. (eds.), "Computational Intelligence Defined - By Everyone !", Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Berlin, Heidelberg: Springer, pp. 10–37, doi:10.1007/978-3-642-58930-0_2, ISBN   978-3-642-63796-4 , retrieved February 1, 2025
  32. 1 2 "IEEE Computational Intelligence Society History". Engineering and Technology history Wiki. July 22, 2014. Retrieved October 30, 2015.
  33. ↑ Bezdek, James C (1994). "What is computational intelligence?". In Zurada, Jacek M.; Marks II, Robert J.; Robinson, Charles J. (eds.). Computational Intelligence: Imitating Life. New York, NY: IEEE Press. pp. 1–12. ISBN   978-0-7803-1104-6.
  34. ↑ Engelbrecht, Andries P. (2007). "Fuzzy Logic and Reasoning". Computational Intelligence: An Introduction (2nd ed.). Chichester, England ; Hoboken, NJ: John Wiley & Sons. p. 465-474. ISBN   978-0-470-03561-0. OCLC   133465571.
  35. 1 2 3 Chai, Wesley. "Fuzzy logic applications". What is Fuzzy Logic? - Definition from SearchEnterpriseAI. Retrieved February 11, 2025.
  36. ↑ Siddique, N. H.; Adeli, Hojjat (2013). "Fuzzy Logic". Computational intelligence: synergies of fuzzy logic, neural networks, and evolutionary computing. Chichester, West Sussex, United Kingdom: John Wiley & Sons Inc. pp. 4–5. ISBN   978-1-118-53481-6.
  37. ↑ Pedrycz, Witold (1993). Fuzzy control and fuzzy systems. Electronic & electrical engineering research studies Control theory and applications series (2., extended, edition, reprint ed.). Taunton: Research Studies Press [u.a.] ISBN   978-0-86380-131-0.
  38. 1 2 Höppner, Frank, ed. (1999). Fuzzy cluster analysis: methods for classification, data analysis, and image recognition. Chichester ; New York: J. Wiley. ISBN   978-0-471-98864-9.
  39. ↑ Dunn, J. C. (1973). "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters". Journal of Cybernetics. 3 (3): 32–57. doi:10.1080/01969727308546046. ISSN   0022-0280.
  40. ↑ Engelbrecht, Andries P. (2007). "Fuzzy Systems". Computational Intelligence: An Introduction (2nd ed.). Chichester, England ; Hoboken, NJ: John Wiley & Sons. p. 10-11. ISBN   978-0-470-03561-0. OCLC   133465571.
  41. 1 2 3 4 Engelbrecht, Andries P. (2007). "Artificial Neural Networks". Computational Intelligence: An Introduction (2nd ed.). Chichester, England ; Hoboken, NJ: John Wiley & Sons. p. 5-7. ISBN   978-0-470-03561-0. OCLC   133465571.
  42. ↑ Eberhart, Russell C.; Shi, Yuhui (2007). "Biological Basis for Neural Networks". Computational Intelligence: Concepts to Implementations. Amsterdam ; Boston: Elsevier/Morgan Kaufmann Publishers. pp. 4–6. doi:10.1016/B978-1-55860-759-0.X5000-8. ISBN   978-1-55860-759-0. OCLC   136781819.
  43. 1 2 3 Siddique, N. H.; Adeli, Hojjat (2013). "Neural Networks". Computational intelligence: synergies of fuzzy logic, neural networks, and evolutionary computing. Chichester, West Sussex, United Kingdom: John Wiley & Sons Inc. p. 5. ISBN   978-1-118-53481-6.
  44. 1 2 Stergiou, Christos; Siganos, Dimitrios. "Neural Networks". SURPRISE 96 Journal. Imperial College London. Archived from the original on December 16, 2009. Retrieved March 11, 2015.
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  46. ↑ De Jong, Kenneth A. (2006). Evolutionary Computation: A Unified Approach. Cambridge, MA: MIT Press. ISBN   978-0-262-52960-0.
  47. ↑ Eiben, A.E.; Smith, J.E. (2015). "Popular Evolutionary Algorithm Variants". Introduction to Evolutionary Computing. Natural Computing Series. Berlin, Heidelberg: Springer. pp. 99–116. doi:10.1007/978-3-662-44874-8. ISBN   978-3-662-44873-1.
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  50. ↑ Siddique, N. H.; Adeli, Hojjat (2013). "Learning Theory". Computational intelligence: synergies of fuzzy logic, neural networks, and evolutionary computing. Chichester, West Sussex, United Kingdom: John Wiley & Sons Inc. p. 6. ISBN   978-1-118-53481-6.
  51. ↑ Worrell, James. "Computational Learning Theory: 2014-2015". University of Oxford. Presentation page of CLT course. Retrieved February 11, 2015.
  52. ↑ Siddique, N. H.; Adeli, Hojjat (2013). "Probabilistic Methods". Computational intelligence: synergies of fuzzy logic, neural networks, and evolutionary computing. Chichester, West Sussex, United Kingdom: John Wiley & Sons Inc. pp. 6–7. ISBN   978-1-118-53481-6.
  53. ↑ Palit, Ajoy K.; Popovic, Dobrivoje (2006). Computational Intelligence in Time Series Forecasting : Theory and Engineering Applications. Springer Science & Business Media. p. 4. ISBN   9781846281846.
  54. ↑ NEES JAN VAN ECK and LUDO WALTMAN (2007). "Bibliometric Mapping of the Computational Intelligence Field". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 15 (5). World Scientific Pub Co Pte Lt: 625–645. doi:10.1142/s0218488507004911. hdl: 1765/10073 .
  55. ↑ Minaie, Afsaneh and Sanati-Mehrizy, Paymon and Sanati-Mehrizy, Ali and Sanati-Mehrizy, Reza (2013). "Computational Intelligence Course in Undergraduate Computer Science and Engineering Curricula" (PDF). Age. 23: 1.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  56. ↑ Mengjie Zhang (2011). "Experience of Teaching Computational Intelligence in an Undergraduate Level Course [Educational Forum]". IEEE Computational Intelligence Magazine. 6 (3). Institute of Electrical and Electronics Engineers (IEEE): 57–59. doi:10.1109/mci.2011.941591.
  57. ↑ Samanta, Biswanath (2011). Computational intelligence: a Tool for Multidisciplinary Education and Research. Proceedings of the 2011 ASEE Northeast Section Annual Conference, University of Hartford.
  58. ↑ G.K.K. Venayagamoorthy (2009). "A successful interdisciplinary course on computational intelligence". IEEE Computational Intelligence Magazine. 4 (1). Institute of Electrical and Electronics Engineers (IEEE): 14–23. doi:10.1109/mci.2008.930983.
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