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Articles de revues sur le sujet « Evolutionary computation applications »

Auteur : Grafiati

Publié le 16 juillet 2024

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1

Hong, Tzung-Pei, Chuan-Kang Ting, and Oliver Kramer. "Theory and Applications of Evolutionary Computation." Applied Computational Intelligence and Soft Computing 2010 (2010): 1–2. http://dx.doi.org/10.1155/2010/360796.

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Easson, Greg, and H. G. Momm. "Evolutionary Computation for Remote Sensing Applications." Geography Compass 4, no. 3 (2010): 172–92. http://dx.doi.org/10.1111/j.1749-8198.2009.00309.x.

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Pinho, Jorge, João Luis Sobral, and Miguel Rocha. "Parallel evolutionary computation in bioinformatics applications." Computer Methods and Programs in Biomedicine 110, no. 2 (2013): 183–91. http://dx.doi.org/10.1016/j.cmpb.2012.10.001.

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Cicirello, Vincent A. "Evolutionary Computation: Theories, Techniques, and Applications." Applied Sciences 14, no. 6 (2024): 2542. http://dx.doi.org/10.3390/app14062542.

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Kozlov, AP. "Biological Computation and Compatibility Search in the Possibility Space as the Mechanism of Complexity Increase During Progressive Evolution." Evolutionary Bioinformatics 18 (January 2022): 117693432211106. http://dx.doi.org/10.1177/11769343221110654.

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The idea of computational processes, which take place in nature, for example, DNA computation, is discussed in the literature. DNA computation that is going on in the immunoglobulin locus of vertebrates shows how the computations in the biological possibility space could operate during evolution. We suggest that the origin of evolutionarily novel genes and genome evolution constitute the original intrinsic computation of the information about new structures in the space of unrealized biological possibilities. Due to DNA computation, the information about future structures is generated and stored in DNA as genetic information. In evolving ontogenies, search algorithms are necessary, which can search for information about evolutionary innovations and morphological novelties. We believe that such algorithms include stochastic gene expression, gene competition, and compatibility search at different levels of structural organization. We formulate the increase in complexity principle in terms of biological computation and hypothesize the possibility of in silico computing of future functions of evolutionarily novel genes.

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Yar, Morteza Husainy, Vahid Rahmati, and Hamid Reza Dalili Oskouei. "A Survey on Evolutionary Computation: Methods and Their Applications in Engineering." Modern Applied Science 10, no. 11 (2016): 131. http://dx.doi.org/10.5539/mas.v10n11p131.

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Evolutionary computation is now an inseparable branch of artificial intelligence and smart methods based on evolutional algorithms aimed at solving different real world problems by natural procedures involving living creatures. It’s based on random methods, regeneration of data, choosing by changing or replacing data within a system such as personal computer (PC), cloud, or any other data center. This paper briefly studies different evolutionary computation techniques used in some applications specifically image processing, cloud computing and grid computing. These methods are generally categorized as evolutionary algorithms and swarm intelligence. Each of these subfields contains a variety of algorithms and techniques which are presented with their applications. This work tries to demonstrate the benefits of the field by presenting the real world applications of these methods implemented already. Among these applications is cloud computing scheduling problem improved by genetic algorithms, ant colony optimization, and bees algorithm. Some other applications are improvement of grid load balancing, image processing, improved bi-objective dynamic cell formation problem, robust machine cells for dynamic part production, integrated mixed-integer linear programming, robotic applications, and power control in wind turbines.

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Doerr, Benjamin, and Thomas Jansen. "Theory of Evolutionary Computation." Algorithmica 59, no. 3 (2010): 299–300. http://dx.doi.org/10.1007/s00453-010-9472-3.

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Zhang, Biaobiao, Yue Wu, Jiabin Lu, and K. L. Du. "Evolutionary Computation and Its Applications in Neural and Fuzzy Systems." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/938240.

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Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

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Holmes, John H. "Methods and applications of evolutionary computation in biomedicine." Journal of Biomedical Informatics 49 (June 2014): 11–15. http://dx.doi.org/10.1016/j.jbi.2014.05.008.

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CHOPARD, BASTIEN, OLIVIER PICTET, and MARCO TOMASSINP. "PARALLEL AND DISTRIBUTED EVOLUTIONARY COMPUTATION FOR FINANCIAL APPLICATIONS." Parallel Algorithms and Applications 15, no. 1-2 (2000): 15–36. http://dx.doi.org/10.1080/01495730008947348.

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Kim, Yong-Hyuk, Ahmed Kattan, Michael Kampouridis, and Yourim Yoon. "Discrete Dynamics in Evolutionary Computation and Its Applications." Discrete Dynamics in Nature and Society 2016 (2016): 1–2. http://dx.doi.org/10.1155/2016/6043597.

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Wang, Youbing, Hao Sun, and Lishan Kang. "The applications of evolutionary computation in software reliability." Wuhan University Journal of Natural Sciences 1, no. 3-4 (1996): 645–50. http://dx.doi.org/10.1007/bf02900900.

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Wang, Fei, Hanghang Tong, and Ching-Yung Lin. "Towards Evolutionary Nonnegative Matrix Factorization." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 501–6. http://dx.doi.org/10.1609/aaai.v25i1.7927.

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Nonnegative Matrix Factorization (NMF) techniques has aroused considerable interests from the field of artificial intelligence in recent years because of its good interpretability and computational efficiency. However, in many real world applications, the data features usually evolve over time smoothly. In this case, it would be very expensive in both computation and storage to rerun the whole NMF procedure after each time when the data feature changing. In this paper, we propose Evolutionary Nonnegative Matrix Factorization (eNMF), which aims to incrementally update the factorized matrices in a computation and space efficient manner with the variation of the data matrix. We devise such evolutionary procedure for both asymmetric and symmetric NMF. Finally we conduct experiments on several real world data sets to demonstrate the efficacy and efficiency of eNMF.

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Murawski, K., T. Arciszewski, and K. De Jong. "Evolutionary Computation in Structural Design." Engineering with Computers 16, no. 3-4 (2000): 275–86. http://dx.doi.org/10.1007/pl00013716.

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TAMAKI, Hisashi. "Emergent System and its Applications. Evolutionary Computation and Optimization." Journal of the Japan Society for Precision Engineering 64, no. 10 (1998): 1439–42. http://dx.doi.org/10.2493/jjspe.64.1439.

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Li, Jian-Yu, Zhi-Hui Zhan, and Jun Zhang. "Evolutionary Computation for Expensive Optimization: A Survey." Machine Intelligence Research 19, no. 1 (2022): 3–23. http://dx.doi.org/10.1007/s11633-022-1317-4.

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AbstractExpensive optimization problem (EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation (EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applications more efficiently.

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Reeves, Colin R. "Evolutionary computation: a unified approach." Genetic Programming and Evolvable Machines 8, no. 3 (2007): 293–95. http://dx.doi.org/10.1007/s10710-007-9035-9.

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Li, Xiaodong, Wenjian Luo, and Xin Yao. "Theoretical foundations of evolutionary computation." Genetic Programming and Evolvable Machines 9, no. 2 (2007): 107–8. http://dx.doi.org/10.1007/s10710-007-9047-5.

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Madar, Janos, Janos Abonyi, and Ferenc Szeifert. "Interactive evolutionary computation in process engineering." Computers & Chemical Engineering 29, no. 7 (2005): 1591–97. http://dx.doi.org/10.1016/j.compchemeng.2004.12.009.

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BENTLEY, PETER. "Special Section: Evolutionary Design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 5 (1999): 325. http://dx.doi.org/10.1017/s0890060499135005.

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This issue of AIEDAM is the second in a series of three “mini” special issues on Evolutionary Design by computers. The papers continue the theme that began in Vol. 13, No. 3, 1999, of using Evolutionary Computation for design problems. The first paper by Eby, Averill, Punch and Goodman provides an excellent overview of the most recent work at Michigan State University on this subject. They describe their work on the optimization of flywheels by an injection island genetic algorithm, and show the importance of minimizing the computation time devoted to evaluation for such real-world applications.

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Liu, Feng, Hanyang Wang, Jiahao Zhang, et al. "EvoGAN: An evolutionary computation assisted GAN." Neurocomputing 469 (January 2022): 81–90. http://dx.doi.org/10.1016/j.neucom.2021.10.060.

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Kwong, Sam, and Qingfu Zhang. "Bridging machine learning and evolutionary computation." Neurocomputing 146 (December 2014): 1. http://dx.doi.org/10.1016/j.neucom.2014.06.051.

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Martínez-Torres, M. R., and S. L. Toral-Marín. "Strategic group identification using evolutionary computation." Expert Systems with Applications 37, no. 7 (2010): 4948–54. http://dx.doi.org/10.1016/j.eswa.2009.12.019.

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Jakobovic, Domagoj, Marko Đurasević, Stjepan Picek, and Bruno Gašperov. "ECF: A C＋＋ framework for evolutionary computation." SoftwareX 27 (September 2024): 101640. http://dx.doi.org/10.1016/j.softx.2024.101640.

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Yao, Xin, and Yong Xu. "Recent Advances in Evolutionary Computation." Journal of Computer Science and Technology 21, no. 1 (2006): 1–18. http://dx.doi.org/10.1007/s11390-006-0001-4.

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Skobtsov, Yu A. "Modern Immunological Models and Their Applications." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 3 (140) (September 2022): 61–77. http://dx.doi.org/10.18698/0236-3933-2022-3-61-77.

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he paper considers main models and algorithms of artificial immune systems, which are related to the evolutionary computation paradigm and used to search for potential solutions, each of which is represented by an artificial lymphocyte. Same as an individual in evolutionary computation, an artificial lymphocyte is most often encoded by a binary string or a vector of real numbers. As far as the main models of artificial immune systems are concerned, the clonal selection algorithm is close to the evolutionary strategy of evolutionary computing, though it uses more powerful mutation operators and is applied mainly to solve numerical and combinatorial optimisation problems. The negative selection algorithm is based on the "friend or foe" recognition principle found in the immune system and is most popular in applications. The paper presents two aspects of the algorithm: 1) the basic concept, that is, expanding the set of "friend" cells; 2) the goal, which is to learn to distinguish between "friend" and "foe" cells, while only "friend" cell samples are available. We consider continuous and discrete network models representing regulated networks of molecules and cells. We note the advantages and disadvantages of these models and their application in the field of computer security, robotics, fraud and malfunction detection, data mining, text analysis, image recognition, bioinformatics, games, planning, etc.

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Al-Muhaideb, Sarab, and Mohamed El Bachir Menai. "Evolutionary computation approaches to the Curriculum Sequencing problem." Natural Computing 10, no. 2 (2011): 891–920. http://dx.doi.org/10.1007/s11047-010-9246-5.

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Wong, Kit Po, and Zhao Yang Dong. "Special issue on evolutionary computation for systems and control applications." International Journal of Systems Science 35, no. 13-14 (2004): 729–30. http://dx.doi.org/10.1080/00207720412331303615.

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KOBAYASHI, Shigenobu. "Emergent System and its Applications. System Synthesis by Evolutionary Computation." Journal of the Japan Society for Precision Engineering 64, no. 10 (1998): 1419–22. http://dx.doi.org/10.2493/jjspe.64.1419.

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Raymer, Michael L. "Book Review: Evolutionary Computation in Bioinformatics." Genetic Programming and Evolvable Machines 6, no. 2 (2005): 229–30. http://dx.doi.org/10.1007/s10710-005-7581-6.

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Naji, Dr Loma Shafiq MOHD. "The Impact of Artificial Intelligence Applications on the Digital Marketing Development on the Telecommunications Companies in Jordan." Webology 19, no. 1 (2022): 854–66. http://dx.doi.org/10.14704/web/v19i1/web19059.

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This research mainly aims to analyze the main effect of the Artificial Intelligence applications; neural networks, expert systems and evolutionary computation, on digital marketing development. Specifically, the aim is to evaluate the new artificial intelligence applications in Jordan, especially those which can affect digital marketing development. Besides, the researcher designed questionnaires which were given on the basis of a technique of simple sampling. Moreover, they were applied on the Jordanian telecommunications companies. A number of 375 questionnaires were distributed. Furthermore, a number of 320 samples were gathered. The result was 85% response rate concerning all the respondents’ responses. The researcher relied on reliability test, descriptive analysis, and multiple regression tests, which were applied to achieve the objectives of this research. Furthermore, this study’s results reflected a positive effect of Artificial Intelligence applications; namely, expert systems, evolutionary computation as well as neural networks, on digital marketing development. However, Jordanian employees are concerned with Artificial Intelligence applications; expert systems, evolutionary computation as well as neural networks, on the basis of their perception’s analysis. Such applications can develop the digital marketing in the Jordanian telecommunications companies. Finally, this study’s results make it clear that companies have to improve those applications to enhance digital marketing world.

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Mrozek, Adam, Wacław Kuś, and Łukasz Sztangret. "Real-time evolutionary optimization of metallurgical processes using ARM microcontroller." Computer Methods in Material Science 16, no. 1 (2016): 20–26. http://dx.doi.org/10.7494/cmms.2016.1.0556.

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The real time (RT) computations with the use of microcontrollers have been present in everyday life for years. They are very useful in e.g. online control of processes due to the ability to determine the proper control in case of any environment changes. The algorithms employed in RT computation must be as simple as possible to meet the imposed time limits. On the other hand, the continuous increase in computational power of modern microcontrollers and embedded platforms causes that more complex algorithms can be performed in the real time. However, during implementation of any algorithm the specific structure and requirements of the microcontroller must be taken into consideration. Another way of fulfilling the time limits of the RT computations is application of metamodel instead of model of controlling process. Within this paper the possibility of application of evolutionary algorithm (EA) to solve three chosen optimization problems in real time using microcontroller of ARM architecture was considered. Analyzed optimization problems were as follows aluminum alloy anti-collision side beam hot stamping process, laminar cooling of dual phase (DP) steel sheets and minimization of the potential energy of the atomic clusters. All computations were performed using two different approaches i.e. low-level and object- oriented approach. Obtained results and drawn conclusions are presented.

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Ling, Sai Ho, and Hak Keung Lam. "Evolutionary Algorithms in Health Technologies." Algorithms 12, no. 10 (2019): 202. http://dx.doi.org/10.3390/a12100202.

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Health technology research brings together complementary interdisciplinary research skills in the development of innovative health technology applications. Recent research indicates that artificial intelligence can help achieve outstanding performance for particular types of health technology applications. An evolutionary algorithm is one of the subfields of artificial intelligence, and is an effective algorithm for global optimization inspired by biological evolution. With the rapidly growing complexity of design issues, methodologies and a higher demand for quality health technology applications, the development of evolutionary computation algorithms for health has become timely and of high relevance. This Special Issue intends to bring together researchers to report the recent findings in evolutionary algorithms in health technology.

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Sayama, Hiroki, and Shelley D. Dionne. "Studying Collective Human Decision Making and Creativity with Evolutionary Computation." Artificial Life 21, no. 3 (2015): 379–93. http://dx.doi.org/10.1162/artl_a_00178.

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We report a summary of our interdisciplinary research project “Evolutionary Perspective on Collective Decision Making” that was conducted through close collaboration between computational, organizational, and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, we generated hypotheses about collective human decision making, using agent-based computer simulations. The hypotheses were then tested through several experiments with real human subjects. Throughout this project, we utilized evolutionary computation (EC) in non-traditional ways—(1) as a theoretical framework for reinterpreting the dynamics of idea generation and selection, (2) as a computational simulation model of collective human decision-making processes, and (3) as a research tool for collecting high-resolution experimental data on actual collaborative design and decision making from human subjects. We believe our work demonstrates untapped potential of EC for interdisciplinary research involving human and social dynamics.

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SARKER, RUHUL, JOARDER KAMRUZZAMAN, and CHARLES NEWTON. "EVOLUTIONARY OPTIMIZATION (EvOpt): A BRIEF REVIEW AND ANALYSIS." International Journal of Computational Intelligence and Applications 03, no. 04 (2003): 311–30. http://dx.doi.org/10.1142/s1469026803001051.

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Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques.

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Wakabayashi, Kenichi, and Masayuki Yamamura. "A Design for Cellular Evolutionary Computation by using Bacteria." Natural Computing 4, no. 3 (2005): 275–92. http://dx.doi.org/10.1007/s11047-004-5236-9.

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Fernández de Vega, Francisco, Gustavo Olague, Leonardo Trujillo, and Daniel Lombraña González. "Customizable execution environments for evolutionary computation using BOINC + virtualization." Natural Computing 12, no. 2 (2012): 163–77. http://dx.doi.org/10.1007/s11047-012-9343-8.

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Loughran, Róisín, and Michael O’Neill. "Evolutionary music: applying evolutionary computation to the art of creating music." Genetic Programming and Evolvable Machines 21, no. 1-2 (2020): 55–85. http://dx.doi.org/10.1007/s10710-020-09380-7.

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Divina, F., and J. S. Aguilar-Ruiz. "Biclustering of expression data with evolutionary computation." IEEE Transactions on Knowledge and Data Engineering 18, no. 5 (2006): 590–602. http://dx.doi.org/10.1109/tkde.2006.74.

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ONO, Isao. "Emergent System and its Applications. Applying Evolutionary Computation to Lens Design." Journal of the Japan Society for Precision Engineering 64, no. 10 (1998): 1443–46. http://dx.doi.org/10.2493/jjspe.64.1443.

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Tauritz, D. R., J. N. Kok, and I. G. Sprinkhuizen-Kuyper. "Adaptive Information Filtering using evolutionary computation." Information Sciences 122, no. 2-4 (2000): 121–40. http://dx.doi.org/10.1016/s0020-0255(99)00123-1.

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Roy, Ranjit, Praghnesh Bhatt, and S. P. Ghoshal. "Evolutionary computation based three-area automatic generation control." Expert Systems with Applications 37, no. 8 (2010): 5913–24. http://dx.doi.org/10.1016/j.eswa.2010.02.014.

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Lee, Wei-Po. "Parallelizing evolutionary computation: A mobile agent-based approach." Expert Systems with Applications 32, no. 2 (2007): 318–28. http://dx.doi.org/10.1016/j.eswa.2005.11.034.

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Mashwani, Wali Khan, Zia Ur Rehman, Maharani A. Bakar, Ismail Koçak, and Muhammad Fayaz. "A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems." Complexity 2021 (March 10, 2021): 1–24. http://dx.doi.org/10.1155/2021/5515701.

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Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity.

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Puechmorel, S., and D. Delahaye. "Order statistics and region-based evolutionary computation." Journal of Global Optimization 59, no. 1 (2013): 107–30. http://dx.doi.org/10.1007/s10898-013-0079-5.

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Zhao, Tian-Fang, Wei-Neng Chen, Xin-Xin Ma, and Xiao-Kun Wu. "Evolutionary Computation in Social Propagation over Complex Networks: A Survey." International Journal of Automation and Computing 18, no. 4 (2021): 503–20. http://dx.doi.org/10.1007/s11633-021-1302-3.

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AbstractSocial propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, social innovation, viral marketing, etc. Simulation and optimization are two major themes in social propagation, where network-based simulation helps to analyze and understand the social contagion, and problem-oriented optimization is devoted to contain or improve the infection results. Though there have been many models and optimization techniques, the matter of concern is that the increasing complexity and scales of propagation processes continuously refresh the former conclusions. Recently, evolutionary computation (EC) shows its potential in alleviating the concerns by introducing an evolving and developing perspective. With this insight, this paper intends to develop a comprehensive view of how EC takes effect in social propagation. Taxonomy is provided for classifying the propagation problems, and the applications of EC in solving these problems are reviewed. Furthermore, some open issues of social propagation and the potential applications of EC are discussed. This paper contributes to recognizing the problems in application-oriented EC design and paves the way for the development of evolving propagation dynamics.

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Ritu Jain, Anjali Dadhich, Roshni Rajput, Dinesh Singh Dhakar,. "Investigating the Role of Machine Leaning and Blockchain for Forecasting Stock Market Trends." Journal of Electrical Systems 20, no. 2s (2024): 721–29. http://dx.doi.org/10.52783/jes.1569.

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There is a sudden deluge of evolutionary approaches in the domain of data processing and computation, which are significantly affecting several facets of applications globally. Artificial intelligence, machine learning and Blockchain happen to be at the forefront of evolutionary computation beating conventional approaches. Finance applications currently are heavily reliant of data driven models. Stock trend analysis happens to be one such approach, which lays the foundation for forecasting decisions to be made. The leeway in such applications is critically small as minimal inaccuracies in forecasting may lead to major losses. This paper presents the current perspective in terms of evolutionary algorithms such as artificial intelligence and Blockchain and how they are transforming software development, the application of machine learning algorithms to regression problems. Finally, the stock trend analysis based on in and out of sample datasets has been performed for standard S&P datasets. A comparative analysis with previous work clearly indicates the improved performance of the proposed work with respect to baseline approaches in the domain.

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Gong, Dijin. "Evolutionary Computation and Artificial Neural Networks for Optimization Problems and Their Applications." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 3 (1998): 485. http://dx.doi.org/10.3156/jfuzzy.10.3_485_2.

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Isasi, Pedro, and Julio C. Hernandez. "Introduction to the Applications of Evolutionary Computation in Computer Security and Cryptography." Computational Intelligence 20, no. 3 (2004): 445–49. http://dx.doi.org/10.1111/j.0824-7935.2004.00244.x.

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Byrski, Aleksander, Rafał Dreżewski, Leszek Siwik, and Marek Kisiel-Dorohinicki. "Evolutionary multi-agent systems." Knowledge Engineering Review 30, no. 2 (2015): 171–86. http://dx.doi.org/10.1017/s0269888914000289.

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AbstractThe aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS). The paper starts with a general introduction describing the background, structure and behaviour of EMAS. EMAS application to solving global optimisation problems is presented in the next section along with its modification targeted at lowering the computation costs by early removing certain agents based on immunological inspirations. Subsequent sections deal with the elitist variant of EMAS aimed at solving multi-criteria optimisation problems, and the co-evolutionary one aimed at solving multi-modal optimisation problems. Each variation of EMAS is illustrated with selected experimental results.

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