bibliography.bib

@book{logical_approach,
author = {Poole, David and Mackworth, Alan and Goebel, Randy},
year = {1998},
month = {01},
pages = {},
title = {Computational Intelligence: A Logical Approach},
isbn = {978-0-19-510270-3}
}

@article{CROSSOVER,
author = {Umbarkar, Dr. Anantkumar and Sheth, P.},
year = {2015},
month = {10},
pages = {},
title = {CROSSOVER OPERATORS IN GENETIC ALGORITHMS: A REVIEW},
volume = {6},
journal = {ICTACT Journal on Soft Computing ( Volume: 6 , Issue: 1 )},
doi = {10.21917/ijsc.2015.0150}
}

@article{SELECTION,
author = "Jebari, Khali",
year = "2013",
month = "11",
title = "Parent Selection Operators for Genetic Algorithms",
volume = "12",
journal = "International Journal of Engineering Research and Technology"
}

@article{KAPLAN201915,
title = "Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence",
journal = "Business Horizons",
volume = "62",
number = "1",
pages = "15 - 25",
year = "2019",
issn = "0007-6813",
doi = "https://doi.org/10.1016/j.bushor.2018.08.004",
url = "http://www.sciencedirect.com/science/article/pii/S0007681318301393",
author = "Andreas Kaplan and Michael Haenlein",
keywords = "Artificial intelligence, Big data, Internet of Things, Expert systems, Machine learning, Deep learning",
abstract = "Artificial intelligence (AI)—defined as a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation—is a topic in nearly every boardroom and at many dinner tables. Yet, despite this prominence, AI is still a surprisingly fuzzy concept and a lot of questions surrounding it are still open. In this article, we analyze how AI is different from related concepts, such as the Internet of Things and big data, and suggest that AI is not one monolithic term but instead needs to be seen in a more nuanced way. This can either be achieved by looking at AI through the lens of evolutionary stages (artificial narrow intelligence, artificial general intelligence, and artificial super intelligence) or by focusing on different types of AI systems (analytical AI, human-inspired AI, and humanized AI). Based on this classification, we show the potential and risk of AI using a series of case studies regarding universities, corporations, and governments. Finally, we present a framework that helps organizations think about the internal and external implications of AI, which we label the Three C Model of Confidence, Change, and Control."
}



@article{DARTHMOUTH,
author = {Mccarthy, J. and Minsky, M. and Rochester, N. and Shannon, C.E.},
year = {2006},
month = {12},
pages = {},
title = {A Proposal for the Dartmouth Summer Research Project on Arti cial Intelligence},
volume = {27},
journal = {AI Magazine}
}

@book{marin2008inteligencia,
  title={Inteligencia artificial. T{\'e}cnicas, m{\'e}todos y aplicaciones},
  author={Marin, R. and Jose, P.},
  isbn={9788448156183},
  url={https://books.google.com.mx/books?id=cB8PPwAACAAJ},
  year={2008},
  publisher={McGraw-Hill Interamericana de Espa{\~n}a S.L.}
}

@article{gradcam,
author = {Rs, Ramprasaath and Cogswell, Michael and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
year = {2019},
month = {10},
pages = {},
title = {Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization},
volume = {128},
journal = {International Journal of Computer Vision},
doi = {10.1007/s11263-019-01228-7}
}

@book{hsu2004behind,
  title={Behind Deep Blue: Building the Computer that Defeated the World Chess Champion},
  author={Hsu, F.H.},
  isbn={9780691118185},
  lccn={2002193079},
  series={Princeton paperbacks},
  url={https://books.google.com.sb/books?id=t71fPwAACAAJ},
  year={2004},
  publisher={Princeton University Press}
}

@article{DBFORBES,
author = {Press, Gil},
year = {2018},
month = {02},
pages = {},
title = {The Brute Force of IBM Deep Blue And Google DeepMind},
volume = {},
journal = {Forbes},
doi = {}
}


@article{CAMPBELL200257,
title = "Deep Blue",
journal = "Artificial Intelligence",
volume = "134",
number = "1",
pages = "57 - 83",
year = "2002",
issn = "0004-3702",
doi = "https://doi.org/10.1016/S0004-3702(01)00129-1",
url = "http://www.sciencedirect.com/science/article/pii/S0004370201001291",
author = "Murray Campbell and A.Joseph Hoane and Feng-hsiung Hsu",
keywords = "Computer chess, Game tree search, Parallel search, Selective search, Search extensions, Evaluation function",
abstract = "Deep Blue is the chess machine that defeated then-reigning World Chess Champion Garry Kasparov in a six-game match in 1997. There were a number of factors that contributed to this success, including: •a single-chip chess search engine,•a massively parallel system with multiple levels of parallelism,•a strong emphasis on search extensions,•a complex evaluation function, and•effective use of a Grandmaster game database. This paper describes the Deep Blue system, and gives some of the rationale that went into the design decisions behind Deep Blue."
}

@article{streichert2002introduction,
  title={Introduction to evolutionary algorithms},
  author={Streichert, Felix},
  year = {2002}
}


@article{CROSSOVER_Spears,
title = "A study of crossover operators in genetic programming",
abstract = "Holland{\textquoteright}s analysis of the sources of power of genetic algorithms has served as guidance for the applications of genetic algorithms for more than 15 years. The technique of applying a recombination operator (crossover) to a population of individuals is a key to that power. Neverless, there have been a number of contradictory results concerning crossover operators with respect to overall performance. Recently, for example, genetic algorithms were used to design neural network modules and their control circuits. In these studies, a genedc algorithm without crossover outperformed a genetic algorithm with crossover. This report re-examines these studies, and concludes that the results were caused by a small population size. New results are presented that illustrate the effectiveness of crossover when the population size is larger. From a performance view, the results indicate that better neural networks can be evolved in a shorter time if the genetic algorithm uses crossover.",
author = "Spears, {William M.} and Vic Anand",
year = "1991",
month = jan,
day = "1",
doi = "10.1007/3-540-54563-8_104",
language = "English (US)",
isbn = "9783540545637",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "409--418",
editor = "Ras, {Zbigniew W.} and Maria Zemankova",
booktitle = "Methodologies for Intelligent Systems - 6th International Symposium, ISMIS 1991, Proceedings",
note = "6th International Symposium on Methodologies for Intelligent Systems, ISMIS 1991 ; Conference date: 16-10-1991 Through 19-10-1991",

}

@article{CROSSOVER_REVIEW,
author = {Kora, Padmavathi and Yadlapalli, Priyanka},
year = {2017},
month = {03},
pages = {34-36},
title = {Crossover Operators in Genetic Algorithms: A Review},
volume = {162},
journal = {International Journal of Computer Applications},
doi = {10.5120/ijca2017913370}
}

@article{DYNAMIC_MUTATION,  author={ {Tzung-Pei Hong} and  {Hong-Shung Wang}},  booktitle={1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929)},   title={A dynamic mutation genetic algorithm},   year={1996},  volume={3},  number={},  pages={2000-2005 vol.3},}

@article{rani2019effectiveness,
  title={On the effectiveness of using elitist genetic algorithm in mutation testing},
  author={Rani, Shweta and Suri, Bharti and Goyal, Rinkaj},
  journal={Symmetry},
  volume={11},
  number={9},
  pages={1145},
  year={2019},
  publisher={Multidisciplinary Digital Publishing Institute}
}

@book{koza1992genetic,
  title={Genetic Programming: On the Programming of Computers by Means of Natural Selection},
  author={Koza, J.R. and Koza, J.R. and Rice, J.P.},
  isbn={9780262111706},
  lccn={92025785},
  series={A Bradford book},
  url={https://books.google.com.mx/books?id=Bhtxo60BV0EC},
  year={1992},
  publisher={Bradford}
}

@book{opensourceLGP,
  title={Open-Source Linear Genetic Programming},
  author={Jed Simson},
  year={2017},
  publisher={Faculty of Computing and Mathematical SciencesUniversity of Waikato, Waikato, New Zealand}
}

@article{luke1997comparison,
  title={A comparison of crossover and mutation in genetic programming},
  author={Luke, Sean and Spector, Lee},
  journal={Genetic Programming},
  volume={97},
  pages={240--248},
  year={1997}
}
@book{polilang08gp,
author = {Poli, Riccardo and Langdon, William and Mcphee, Nicholas},
year = {2008},
month = {01},
pages = {},
title = {A Field Guide to Genetic Programming},
isbn = {978-1-4092-0073-4}
}

@article{LCS_smith,
author="SMITH, S. F.",
title="A Learning system based on genetic adaptive algorithms",
journal="Ph. D. Thesis, Univ. of Pittsburgh",
ISSN="",
publisher="",
year="1980",
month="",
volume="",
number="",
pages="",
URL="https://ci.nii.ac.jp/naid/10010118042/en/",
DOI="",
}

@book{HOLLAND1978313,
title = "COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS11Research reported in this paper was supported in part by the National Science Foundation under grant DCR 71-01997 and by the Horace H. Rackham School of Graduate Studies under grant 387156.",
editor = "D.A. WATERMAN and FREDERICK HAYES-ROTH",
booktitle = "Pattern-Directed Inference Systems",
publisher = "Academic Press",
pages = "313 - 329",
year = "1978",
isbn = "978-0-12-737550-2",
doi = "https://doi.org/10.1016/B978-0-12-737550-2.50020-8",
url = "http://www.sciencedirect.com/science/article/pii/B9780127375502500208",
author = "John H. Holland and Judith S. Reitman",
abstract = "The type of cognitive system (CS) studíed here has four basic parts: (1) a set of interacting elementary productions, called lassifiers, (2) a performance algorithm that directs the action of the system in the environment, (3) a simple learning algorithm that keeps a record of each classifier's success in bringing about rewards, and (4) a more complex learning algorithm, called the genetic algorithm, that modifies the set of classifiers so that variants of good classifiers persist and new, potentially better ones are created in a provably efficient manner. Two “proof-of-principle” experiments are reported. One experiment shows CS's performance in a maze when it has only the ability to adjust the predictions about ensuing rewards of classifiers (similar to adjusting the “weight” of each classifier) vs. when the power of the genetic algorithm is added. Criterion was achieved an order of magnitude more rapidly when the genetic algorithm was operative. A second experiment examines transfer of learning. Placed in a more difficult maze, CS with experience in the simpler maze reaches criterion an order of magnitude more rapidly than CS without prior experience."
}

@article{SOW_LCS_SURVEY,
author = {Sigaud, Olivier and Wilson, Stewart},
year = {2007},
month = {05},
pages = {1065-1078},
title = {Learning classifier systems: A survey},
volume = {11},
journal = {Soft Comput.},
doi = {10.1007/s00500-007-0164-0}
}

@inproceedings{LCS_LBGA,
author = {Bacardit, Jaume and Bernadó-Mansilla, Ester and Butz, Martin},
year = {2007},
month = {01},
pages = {1-21},
title = {Learning Classifier Systems: Looking Back and Glimpsing Ahead},
volume = {4998},
doi = {10.1007/978-3-540-88138-4_1}
}

@article{UrbanowiczLCS,
author = {Urbanowicz, Ryan and Moore, Jason},
year = {2009},
month = {09},
pages = {},
title = {Learning Classifier Systems: A Complete Introduction, Review, and Roadmap},
volume = {2009},
journal = {Journal of Artificial Evolution and Applications},
doi = {10.1155/2009/736398}
}

@Book{rug01_000857792,
author={Langley, Pat},
title={Elements of machine learning},
year={1996},
publisher={San Francisco (Calif.) : Morgan Kaufmann},
isbn={1558603018},
url={http://lib.ugent.be/catalog/rug01:000857792},
language={eng}
}

@article{wilste_zcs,
author = {Wilson, Stewart},
year = {1970},
month = {02},
pages = {},
title = {ZCS: A zeroth level classifier system},
volume = {2},
journal = {Evolutionary Computation},
doi = {10.1162/evco.1994.2.1.1}
}

@article{cadrik_zcs,
author={C{\'a}drik, Tom{\'a}{\v{s}} and Mach, Marian},
editor={Sin{\v{c}}{\'a}k, Peter
and Hartono, Pitoyo
and Vir{\v{c}}{\'i}kov{\'a}, M{\'a}ria
and Va{\v{s}}{\v{c}}{\'a}k, J{\'a}n
and Jak{\v{s}}a, Rudolf},
title={Usage of ZCS Evolutionary Classifier System as a Rule Maker for Cleaning Robot Task},
booktitle={Emergent Trends in Robotics and Intelligent Systems},
year={2015},
publisher={Springer International Publishing},
address={Cham},
pages={113-119},
abstract={This paper introduces the Cleaning robot task which is a simulation of the cleaning of a room by a robot. The robot must collect all the junk in the room and put it into a container. It must take out the junk sequentially, because the amount of carried trash is limited. The actions of this robot are selected by using the Michigan style classifier system ZCS. This paper shows the capability of this system to select good rules for the robot to perform the cleaning task.},
isbn={978-3-319-10783-7}
}

@article{turing_compmach,
    author = {TURING, A. M.},
    title = "{I.—COMPUTING MACHINERY AND INTELLIGENCE}",
    journal = {Mind},
    volume = {LIX},
    number = {236},
    pages = {433-460},
    year = {1950},
    month = {10},
    issn = {0026-4423},
    doi = {10.1093/mind/LIX.236.433},
    url = {https://doi.org/10.1093/mind/LIX.236.433},
    eprint = {https://academic.oup.com/mind/article-pdf/LIX/236/433/30123314/lix-236-433.pdf},
}
@article{orriols2006further,
  title={A further look at UCS classifier system},
  author={Orriols-Puig, Albert and Bernad{\'o}-Mansilla, Ester},
  year={2006}
}

@article{slossEA,
author = {Sloss, Andrew and Gustafson, Steven},
year = {2019},
month = {06},
pages = {},
title = {2019 Evolutionary Algorithms Review}
}
@article{munoz2013introduccion,
  title={Introducci{\'o}n a la l{\'o}gica},
  author={Mu{\~n}oz Guti{\'e}rrez, Carlos},
  journal={Recuperado de http://pendientedemigracion. ucm. es/info/pslogica/cdn. pdf},
  year={2013}
}

@book{flasinski2016symbolic,
  title={Symbolic Artificial Intelligence},
  author={Flasi{\'n}ski, Mariusz},
  booktitle={Introduction to Artificial Intelligence},
  pages={15--22},
  year={2016},
  publisher={Springer}
}

@article{GARNELO201917,
title = "Reconciling deep learning with symbolic artificial intelligence: representing objects and relations",
journal = "Current Opinion in Behavioral Sciences",
volume = "29",
pages = "17 - 23",
year = "2019",
note = "SI: 29: Artificial Intelligence (2019)",
issn = "2352-1546",
doi = "https://doi.org/10.1016/j.cobeha.2018.12.010",
url = "http://www.sciencedirect.com/science/article/pii/S2352154618301943",
author = "Marta Garnelo and Murray Shanahan",
abstract = "In the history of the quest for human-level artificial intelligence, a number of rival paradigms have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks. However, both paradigms have strengths and weaknesses, and a significant challenge for the field today is to effect a reconciliation. A central tenet of the symbolic paradigm is that intelligence results from the manipulation of abstract compositional representations whose elements stand for objects and relations. If this is correct, then a key objective for deep learning is to develop architectures capable of discovering objects and relations in raw data, and learning how to represent them in ways that are useful for downstream processing. This short review highlights recent progress in this direction."
}

@book{mccorduck2004machines,
  title={Machines who think: A personal inquiry into the history and prospects of artificial intelligence},
  author={McCorduck, Pamela and Cfe, Cli},
  year={2004},
  pages ={124},
  publisher={CRC Press}
}

@book{guerra_repcon,
  title={Representación del Conocimiento},
  author={Alejandro Guerra Hernández},
  pages={},
  year={2018}
}

@book{russell2004inteligencia,
  title={Inteligencia artificial: un enfoque moderno},
  author={Russell, S.J. and Norvig, P. and Rodríguez, J.M.C.},
  isbn={9788420540030},
  series={Colección de Inteligencia Artificial de Prentice Hall},
  year={2004},
  publisher={Pearson Educación}
}

@book{navarro_prolog,
  title={Curso de programación lógica},
  author={Marisa Navarro},
  publisher={Facultad de Informática de San Sebastián},
  year={2008}
}

@book{tms_ES,
author = {Tolun, Mehmet and Sahin, Seda and Oztoprak, Kasim},
year = {2016},
month = {12},
pages = {},
title = {Expert Systems},
doi = {10.1002/0471238961.0524160518011305.a01.pub2}
}

@article{leph_ES,
author = {Leith, Philip},
year = {2016},
month = {09},
pages = {94-106},
title = {The rise and fall of the legal expert system Previously published in Leith P., ‘The rise and fall of the legal expert system’, in European Journal of Law and Technology, Vol 1, Issue 1, 2010.View all notes},
volume = {30},
journal = {International Review of Law, Computers and Technology},
doi = {10.1080/13600869.2016.1232465}
}

@book{mitchell1997machine,
  title={Machine Learning},
  author={Mitchell, T.M.},
  isbn={9780071154673},
  lccn={97007692},
  series={McGraw-Hill International Editions},
  url={https://books.google.com.mx/books?id=EoYBngEACAAJ},
  year={1997},
  publisher={McGraw-Hill}
}

@book{burkov2019hundred,
  title={The Hundred-Page Machine Learning Book},
  author={Burkov, A.},
  isbn={9781999579517},
  url={https://books.google.com.mx/books?id=0jbxwQEACAAJ},
  year={2019},
  publisher={Andriy Burkov}
}

@article{schmidt2005least,
  title={Least squares optimization with L1-norm regularization},
  author={Schmidt, Mark},
  journal={CS542B Project Report},
  volume={504},
  pages={195--221},
  year={2005},
  publisher={University of British Columbia}
}

@article{ruder2017overview,
      title={An overview of gradient descent optimization algorithms}, 
      author={Sebastian Ruder},
      year={2017},
      eprint={1609.04747},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@article{knn_intro,
  title={An introduction to kernel and nearest-neighbor nonparametric regression},
  author={Altman, Naomi S},
  journal={The American Statistician},
  volume={46},
  number={3},
  pages={175--185},
  year={1992},
  publisher={Taylor \& Francis Group}
}
@article{bayes1958essay,
  title={Essay towards solving a problem in the doctrine of chances},
  author={Bayes, Thomas},
  journal={Biometrika},
  volume={45},
  pages={293--315},
  year={1958}
}

@article{dtc_survey,
  author={S. R. {Safavian} and D. {Landgrebe}},
  journal={IEEE Transactions on Systems, Man, and Cybernetics}, 
  title={A survey of decision tree classifier methodology}, 
  year={1991},
  volume={21},
  number={3},
  pages={660-674},
  doi={10.1109/21.97458}
}
@article{boswell2002introduction,
  title={Introduction to support vector machines},
  author={Boswell, Dustin},
  journal={Departement of Computer Science and Engineering University of California San Diego},
  year={2002}
}

@book{prince2023understanding,
 author = "Simon J.D. Prince",
 title = "Understanding Deep Learning",
 publisher = "MIT Press",
 year = 2023,
 url = "http://udlbook.com"
}

@article{Xu2015,
  author  = {Xu, Dongkuan and Tian, Yingjie},
  year    = {2015},
  month   = {06/01},
  title   = {A Comprehensive Survey of Clustering Algorithms},
  journal = {Annals of Data Science},
  volume  = {2},
  number  = {2},
  pages   = {165-193},
  abstract = {Data analysis is used as a common method in modern science research, which is across communication science, computer science, and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.},
  issn    = {2198-5812},
  url     = {https://doi.org/10.1007/s40745-015-0040-1},
  doi     = {10.1007/s40745-015-0040-1}
}