Artykuły w czasopismach: „Evolutionary programming (computer science)”

1

Vitányi, Paul. "A discipline of evolutionary programming". Theoretical Computer Science 241, nr 1-2 (czerwiec 2000): 3–23. http://dx.doi.org/10.1016/s0304-3975(99)00263-7.

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Doerr, Benjamin, Anton Eremeev, Frank Neumann, Madeleine Theile i Christian Thyssen. "Evolutionary algorithms and dynamic programming". Theoretical Computer Science 412, nr 43 (październik 2011): 6020–35. http://dx.doi.org/10.1016/j.tcs.2011.07.024.

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DU, Haifeng. "Adaptive chaos clonal evolutionary programming algorithm". Science in China Series F 48, nr 5 (2005): 579. http://dx.doi.org/10.1360/04yf0458.

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Ji, Mingjun, Huanwen Tang i Juan Guo. "A single-point mutation evolutionary programming". Information Processing Letters 90, nr 6 (czerwiec 2004): 293–99. http://dx.doi.org/10.1016/j.ipl.2004.03.002.

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English, T. M. "Evolutionary Programming VI[Book Reviews]". IEEE Transactions on Evolutionary Computation 1, nr 3 (wrzesień 1997): 218–19. http://dx.doi.org/10.1109/tevc.1997.661555.

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Kushchu, I. "Genetic programming and evolutionary generalization". IEEE Transactions on Evolutionary Computation 6, nr 5 (październik 2002): 431–42. http://dx.doi.org/10.1109/tevc.2002.805038.

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Nölting, Bengt, Dennis Jülich, Winfried Vonau i Karl Andert. "Evolutionary Computer Programming of Protein Folding and Structure Predictions". Journal of Theoretical Biology 229, nr 1 (lipiec 2004): 13–18. http://dx.doi.org/10.1016/j.jtbi.2004.02.016.

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Jong-Hwan Kim i Kwang-Choon Kim. "Multicriteria fuzzy control using evolutionary programming". Information Sciences 103, nr 1-4 (grudzień 1997): 71–86. http://dx.doi.org/10.1016/s0020-0255(97)00050-9.

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Mallipeddi, R., S. Mallipeddi i P. N. Suganthan. "Ensemble strategies with adaptive evolutionary programming". Information Sciences 180, nr 9 (maj 2010): 1571–81. http://dx.doi.org/10.1016/j.ins.2010.01.007.

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Farach, Martin, i Mikkel Thorup. "Sparse Dynamic Programming for Evolutionary-Tree Comparison". SIAM Journal on Computing 26, nr 1 (luty 1997): 210–30. http://dx.doi.org/10.1137/s0097539794262422.

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Oplatková, Zuzana, i Ivan Zelinka. "Investigation on Evolutionary Synthesis of Movement Commands". Modelling and Simulation in Engineering 2009 (2009): 1–12. http://dx.doi.org/10.1155/2009/845080.

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This paper deals with usage of an alternative tool for symbolic regression—analytic programming which is able to solve various problems from the symbolic domain, as well as genetic programming and grammatical evolution. This paper describes a setting of an optimal trajectory for a robot (originally designed as an artificial ant on Santa Fe trail) solved by means of analytic programming. Firstly, main principles of analytic programming are described and explained. The second part shows how analytic programming was used for the application of finding a suitable trajectory step by step. Because analytic programming needs evolutionary algorithms for its run, three evolutionary algorithms were used—self-organizing migrating algorithm, differential evolution, and simulated annealing—to show that anyone can be used. The total number of simulations was 150 and results show that the first two used algorithms were more successful than not so robust simulated annealing.

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Kovacic, M., M. Brezocnik, I. Pahole, J. Balic i B. Kecelj. "Evolutionary programming of CNC machines". Journal of Materials Processing Technology 164-165 (maj 2005): 1379–87. http://dx.doi.org/10.1016/j.jmatprotec.2005.02.047.

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Naveed, Muhammad Shumail. "Comparison of C++ and Java in Implementing Introductory Programming Algorithms". Quaid-e-Awam University Research Journal of Engineering Science & Technology 19, nr 1 (30.06.2021): 95–103. http://dx.doi.org/10.52584/qrj.1901.14.

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Computer programming is the heart of computer science and C++ and Java are the most popular and widely used languages. The performance of C++ and Java programming languages has been benchmarked using robotics, numerical, genetic and evolutionary algorithms, but not using introductory programming algorithms. This article examined C++ and Java by analysing the implementation of novice algorithms of introductory programming courses by evaluating the difficulty, effort, time and delivered bugs of programs with Halstead software metrics. The study identified that Java is more difficult than C++ in the implementation of selected algorithms. Similarly, the efforts required to translate the novice algorithms in Java are higher than C++. The time involved in translating selected algorithms in C++ is lower than Java. Similarly, the number of delivered bugs in C++ is lower than Java. The study suggests that C++ is more suitable than Java for the implementation of introductory programming algorithms and reasonably more suitable for introductory programming courses. The study emboldens the programming linguists to do further analyses in contemplating other programming languages with Halstead software metrics and other kinds of algorithms.

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Li, Xiao Guang. "Research on the Development and Applications of Artificial Neural Networks". Applied Mechanics and Materials 556-562 (maj 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.

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Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.

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ZHANG, H., i J. LU. "Adaptive evolutionary programming based on reinforcement learning". Information Sciences 178, nr 4 (15.02.2008): 971–84. http://dx.doi.org/10.1016/j.ins.2007.09.026.

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Jong-Hwan Kim i Hyun Myung. "Evolutionary programming techniques for constrained optimization problems". IEEE Transactions on Evolutionary Computation 1, nr 2 (lipiec 1997): 129–40. http://dx.doi.org/10.1109/4235.687880.

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Thie, C. J., i C. Giraud-Carrier. "Learning concept descriptions with typed evolutionary programming". IEEE Transactions on Knowledge and Data Engineering 17, nr 12 (grudzień 2005): 1664–77. http://dx.doi.org/10.1109/tkde.2005.199.

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Françoso Dal Piccol Sotto, Léo, Paul Kaufmann, Timothy Atkinson, Roman Kalkreuth i Márcio Porto Basgalupp. "Graph representations in genetic programming". Genetic Programming and Evolvable Machines 22, nr 4 (30.09.2021): 607–36. http://dx.doi.org/10.1007/s10710-021-09413-9.

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AbstractGraph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and $$(1+\lambda )$$ ( 1 + λ ) . In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the ($$1 + \lambda$$ 1 + λ ) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.

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Soh, Chee Kiong, i Yue Xing Dong. "Evolutionary Programming for Inverse Problems in Civil Engineering". Journal of Computing in Civil Engineering 15, nr 2 (kwiecień 2001): 144–50. http://dx.doi.org/10.1061/(asce)0887-3801(2001)15:2(144).

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Huang, Han, Shujin Ye, Zhun Fan, Zhiyong Lin, Liang Lv i Zhifeng Hao. "Evolutionary programming with a simulated-conformist mutation strategy". Soft Computing 22, nr 2 (12.10.2016): 659–76. http://dx.doi.org/10.1007/s00500-016-2365-x.

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Dong, Hongbin, Yuxin Dong, Cheng Zhou, Guisheng Yin i Wei Hou. "A fuzzy clustering algorithm based on evolutionary programming". Expert Systems with Applications 36, nr 9 (listopad 2009): 11792–800. http://dx.doi.org/10.1016/j.eswa.2009.04.031.

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Pedrycz, Witold. "Evolutionary programming V, proceedings of the 5th annual conference on evolutionary programming, San Diego CA, 29 February–2 March, 1996". Neurocomputing 17, nr 2 (październik 1997): 133–34. http://dx.doi.org/10.1016/0925-2312(97)90021-7.

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Shelokar, Prakash, Arnaud Quirin i Óscar Cordón. "Three-objective subgraph mining using multiobjective evolutionary programming". Journal of Computer and System Sciences 80, nr 1 (luty 2014): 16–26. http://dx.doi.org/10.1016/j.jcss.2013.03.005.

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DeLisle, Robert Kirk, i Steven L. Dixon. "Induction of Decision Trees via Evolutionary Programming". Journal of Chemical Information and Computer Sciences 44, nr 3 (maj 2004): 862–70. http://dx.doi.org/10.1021/ci034188s.

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Haubelt, Christian, Luise Müller, Kai Neubauer, Torsten Schaub i Philipp Wanko. "Evolutionary System Design with Answer Set Programming". Algorithms 16, nr 4 (24.03.2023): 179. http://dx.doi.org/10.3390/a16040179.

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We address the problem of evolutionary system design (ESD) by means of answer set programming modulo difference constraints (AMT). The goal of this design approach is to synthesize new product variants or generations from existing products. We start by formalizing the underlying system synthesis problem and design space exploration process, which consists of finding the Pareto front with respect to latency, cost, energy, and similarity measures between the two designs. We then present AMT-based encodings to capture all of these aspects. The idea is to use plain ASP for conflict detection and resolution and for routing and to use difference constraints for scheduling. Moreover, we propose a new approach for expressing the similarity that we use at three alternative levels of AMT-based design space exploration, namely, at the strategic, heuristic, and objective levels, which is performed to guide the exploration towards designs of high interest. Last but not least, we systematically evaluate the emerging techniques empirically and identify the most promising AMT techniques.

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Greeff, D. J., i C. Aldrich. "Empirical modelling of chemical process systems with evolutionary programming". Computers & Chemical Engineering 22, nr 7-8 (lipiec 1998): 995–1005. http://dx.doi.org/10.1016/s0098-1354(97)00271-8.

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Michalewicz, Z. "Evolutionary computation techniques for nonlinear programming problems". International Transactions in Operational Research 1, nr 2 (kwiecień 1994): 223–40. http://dx.doi.org/10.1016/0969-6016(94)90022-1.

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Michalewicz, Zbigniew. "Evolutionary Computation Techniques for Nonlinear Programming Problems". International Transactions in Operational Research 1, nr 2 (kwiecień 1994): 223–40. http://dx.doi.org/10.1111/1475-3995.d01-23.

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Chang, Jen-Fuh, Yi-Sung Yang, Teh-Lu Liao i Jun-Juh Yan. "Parameter identification of chaotic systems using evolutionary programming approach". Expert Systems with Applications 35, nr 4 (listopad 2008): 2074–79. http://dx.doi.org/10.1016/j.eswa.2007.09.021.

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Pawlak, Tomasz P. "Synthesis of Mathematical Programming models with one-class evolutionary strategies". Swarm and Evolutionary Computation 44 (luty 2019): 335–48. http://dx.doi.org/10.1016/j.swevo.2018.04.007.

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Csukás, Béla, i Sándor Balogh. "Combining genetic programming with generic simulation models in evolutionary synthesis". Computers in Industry 36, nr 3 (czerwiec 1998): 181–97. http://dx.doi.org/10.1016/s0166-3615(98)00071-2.

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Villaescusa, Sara Lledó, i Rafael Lahoz-Beltra. "Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria". Algorithms 16, nr 12 (17.12.2023): 571. http://dx.doi.org/10.3390/a16120571.

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At present, synthetic biology applications are based on the programming of synthetic bacteria with custom-designed genetic circuits through the application of a top-down strategy. These genetic circuits are the programs that implement a certain algorithm, the bacterium being the agent or shell responsible for the execution of the program in a given environment. In this work, we study the possibility that instead of programming synthesized bacteria through a custom-designed genetic circuit, it is the circuit itself which emerges as a result of the evolution simulated through an evolutionary algorithm. This study is conducted by performing in silico experiments in a community composed of synthetic bacteria in which one species or strain behaves as pathogenic bacteria against the rest of the non-pathogenic bacteria that are also part of the bacterial consortium. The goal is the eradication of the pathogenic strain through the evolutionary programming of the agents or synthetic bacteria. The results obtained suggest the plausibility of the evolutionary design of the appropriate genetic circuit resulting from the application of a bottom-up strategy and therefore the experimental feasibility of the evolutionary programming of synthetic bacteria.

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Hamam, Haneen, Ali Raza, Manal M. Alqarni, Jan Awrejcewicz, Muhammad Rafiq, Nauman Ahmed, Emad E. Mahmoud, Witold Pawłowski i Muhammad Mohsin. "Stochastic Modelling of Lassa Fever Epidemic Disease". Mathematics 10, nr 16 (13.08.2022): 2919. http://dx.doi.org/10.3390/math10162919.

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Evolutionary approaches have a critical role in different disciplines such as real-world problems, computer programming, machine learning, biological sciences, and many more. The design of the stochastic model is based on transition probabilities and non-parametric techniques. Positivity, boundedness, and equilibria are investigated in deterministic and stochastic senses. An essential tool, Euler–Maruyama, is studied for the solution of said model. Standard and nonstandard evolutionary approaches are presented for the stochastic model in terms of efficiency and low-cost approximations. The standard evolutionary procedures like stochastic Euler–Maruyama and stochastic Runge–Kutta fail to restore the essential features of biological problems. On the other hand, the proposed method is efficient, of meager cost, and adopts all the desired feasible properties. At the end of this paper the comparison section is presented to support efficient analysis.

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Kabré, Harouna, i Anne Spalanzani. "EVERA: an evolutionary programming environment for adaptive speech processing". Information Sciences 122, nr 2-4 (luty 2000): 185–200. http://dx.doi.org/10.1016/s0020-0255(99)00125-5.

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FREY, CLEMENS. "CO-EVOLUTION OF FINITE STATE MACHINES FOR OPTIMIZATION: PROMOTION OF DEVICES WHICH SEARCH GLOBALLY". International Journal of Computational Intelligence and Applications 02, nr 01 (marzec 2002): 1–16. http://dx.doi.org/10.1142/s1469026802000397.

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In this work a co-evolutionary approach is used in conjunction with Genetic Programming operators in order to find certain transition rules for two-step discrete dynamical systems. This issue is similar to the well-known artificial-ant problem. We seek the dynamic system to produce a trajectory leading from given initial values to a maximum of a given spatial functional.This problem is recast into the framework of input-output relations for controllers, and the optimization is performed on program trees describing input filters and finite state machines incorporated by these controllers simultaneously. In the context of Genetic Programming there is always a set of test cases which has to be maintained for the evaluation of program trees. These test cases are subject to evolution here, too, so we employ a so-called host-parasitoid model in order to evolve optimizing dynamical systems.Reinterpreting these systems as algorithms for finding the maximum of a functional under constraints, we have derived a paradigm for the automatic generation of adapted optimization algorithms via optimal control. We provide numerical examples generated by the GP-system MathEvEco. These examples refer to key properties of the resulting strategies and they include statistical evidence showing that for this problem of system identification the co-evolutionary approach is superior to standard Genetic Programming.

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Martin, Andrew. "To Tidy or not When Teaching R Skills in Biology Classes". International Journal of Higher Education 11, nr 5 (30.03.2022): 39. http://dx.doi.org/10.5430/ijhe.v11n5p39.

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An essential skill for STEM undergraduates is the ability to understand the world by manipulating, visualizing, and analyzing data to make or evaluate claims. Current online debate, without peer-reviewed literature, explores which of two common R syntax environments (base R or tidyverse) is best for teaching novice R users. In an in-person undergraduate course on evolutionary biology, we implemented two coding curricula: one using base R (n = 49 students) and the other using tidyverse (n = 58 students). We compared these two curricula using several dimensions of student success: interpretation of syntax, creation of appropriate data visualizations and analyses, and an absence of sex bias in performance. A linear model revealed prior experience had the largest estimated effect, followed by syntax environment; sex had the smallest effect. Pedagogical approaches that ensure students have repeated opportunities for practice and that implement techniques to overcome student frustration and anxiety are likely more important than syntax environment when learning coding in biology classes. Furthermore, the small effect of sex combined with the high proportion of females in the biological sciences suggests introducing computer programming in biology may allow females to discover interest and ability that they may not have had if computer programming was the sole propriety of computer science departments.

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Alfonseca, Manuel, i Alfonso Ortega. "Book Review: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language". Genetic Programming and Evolvable Machines 5, nr 4 (grudzień 2004): 393. http://dx.doi.org/10.1023/b:genp.0000036057.27304.5b.

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Boukhelifa, Nadia, i Evelyne Lutton. "Guest editorial: Special issue on genetic programming, evolutionary computation and visualization". Genetic Programming and Evolvable Machines 19, nr 3 (11.08.2018): 313–15. http://dx.doi.org/10.1007/s10710-018-9333-4.

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Álvarez, Josefa, Franciso Chávez, Pedro Castillo, Juan García, Francisco Rodriguez i Vega de. "A fuzzy rule-based system to predict energy consumption of genetic programming algorithms". Computer Science and Information Systems 15, nr 3 (2018): 635–54. http://dx.doi.org/10.2298/csis180110026a.

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In recent years, the energy-awareness has become one of the most interesting areas in our environmentally conscious society. Algorithm designers have been part of this, particularly when dealing with networked devices and, mainly, when handheld ones are involved. Although studies in this area has increased, not many of them have focused on Evolutionary Algorithms. To the best of our knowledge, few attempts have been performed before for modeling their energy consumption considering different execution devices. In this work, we propose a fuzzy rulebased system to predict energy comsumption of a kind of Evolutionary Algorithm, Genetic Prohramming, given the device in wich it will be executed, its main parameters, and a measurement of the difficulty of the problem addressed. Experimental results performed show that the proposed model can predict energy consumption with very low error values.

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Redondo, J. L., I. García i P. M. Ortigosa. "Parallel evolutionary algorithms based on shared memory programming approaches". Journal of Supercomputing 58, nr 2 (18.12.2009): 270–79. http://dx.doi.org/10.1007/s11227-009-0374-6.

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Singh, Nirbhow Jap, Shakti Singh, Vikram Chopra, Mohd Asim Aftab, S. M. Suhail Hussain i Taha Selim Ustun. "Chaotic Evolutionary Programming for an Engineering Optimization Problem". Applied Sciences 11, nr 6 (18.03.2021): 2717. http://dx.doi.org/10.3390/app11062717.

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The aim of the current paper is to present a mimetic algorithm called the chaotic evolutionary programming Powell’s pattern search (CEPPS) algorithm for the solution of the multi-fuel economic load dispatch problem. In the CEPPS algorithm, the exploration process is maintained by chaotic evolutionary programming, whereas exploitation is taken care off by a pattern search. The proposed CEPPS has two variants based on the Gauss map and the tent map. Seven generalized benchmark test functions and six cases of the multi-fuel economic load dispatch problem are considered for the performance analysis. It is observed from the analysis that the CEPPS solution procedure based on the tent map exhibits superiority to obtain an excellent solution and better convergence characteristics than traditional chaotic evolutionary programming. Further, the performance investigation for the considered economic load dispatch shows that the Gauss map CEPPS solution procedure performs better than the tent map based CEPPS to obtain the solution of the multi-fuel economic dispatch problem.

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HU, Zhenjiang, Shin-Cheng MU i Stephanie WEIRICH. "Advanced programming techniques for construction of robust, generic and evolutionary programs". Progress in Informatics, nr 10 (marzec 2013): 1. http://dx.doi.org/10.2201/niipi.2013.10.1.

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Lim, H. S., M. V. C. Rao, A. W. C. Tan i H. T. Chuah. "Multiuser detection for DS-CDMA systems using evolutionary programming". IEEE Communications Letters 7, nr 3 (marzec 2003): 101–3. http://dx.doi.org/10.1109/lcomm.2002.808372.

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Izadkhah, Habib, Islam Elgedawy i Ayaz Isazadeh. "E-CDGM: An Evolutionary Call-Dependency Graph Modularization Approach for Software Systems". Cybernetics and Information Technologies 16, nr 3 (1.09.2016): 70–90. http://dx.doi.org/10.1515/cait-2016-0035.

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Abstract Lack of up-to-date software documentation hinders the software evolution and maintenance processes, as simply the outdated software structure and code could be easily misunderstood. One approach to overcoming such problems is using software modularization, in which the software architecture is extracted from the available source code; such that developers can assess the reconstructed architecture against the required changes. Unfortunately, existing software modularization approaches are not accurate, as they ignore polymorphic calls among system modules. Furthermore, they are tightly coupled to the used programming language. To overcome such problems, this paper proposes the E-CDGM approach. E-CDGM decouples the extracted call dependency graph from the programming language by using the proposed intermediate code language (known as mCode). It also takes into consideration the polymorphic calls during the call dependency graph generation. It uses a new evolutionary optimization approach to find the best modularization option; adopting reward and penalty functions. Finally, it uses statistical analysis to build a final consolidated modularization model using different generated modularization solutions. Experimental results show that the proposed E-CDGM approach provides more accurate results when compared against existing well-known modularization approaches.

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Pedroso, Joao Pedro. "An Evolutionary Solver for Pure Integer Linear Programming". International Transactions in Operational Research 9, nr 3 (maj 2002): 337–52. http://dx.doi.org/10.1111/1475-3995.00359.

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Yang, Bin, Sanrong Liu i Wei Zhang. "Reverse engineering of gene regulatory network using restricted gene expression programming". Journal of Bioinformatics and Computational Biology 14, nr 05 (październik 2016): 1650021. http://dx.doi.org/10.1142/s0219720016500219.

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Inference of gene regulatory networks has been becoming a major area of interest in the field of systems biology over the past decade. In this paper, we present a novel representation of S-system model, named restricted gene expression programming (RGEP), to infer gene regulatory network. A new hybrid evolutionary algorithm based on structure-based evolutionary algorithm and cuckoo search (CS) is proposed to optimize the architecture and corresponding parameters of model, respectively. Two synthetic benchmark datasets and one real biological dataset from SOS DNA repair network in E. coli are used to test the validity of our method. Experimental results demonstrate that our proposed method performs better than previously proposed popular methods.

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Shelokar, Prakash, Arnaud Quirin i Óscar Cordón. "A multiobjective evolutionary programming framework for graph-based data mining". Information Sciences 237 (lipiec 2013): 118–36. http://dx.doi.org/10.1016/j.ins.2013.02.014.

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Qi, Weihua, Wenyuan Yang, Lining Xing i Feng Yao. "Modeling and Solving for Multi-Satellite Cooperative Task Allocation Problem Based on Genetic Programming Method". Mathematics 10, nr 19 (2.10.2022): 3608. http://dx.doi.org/10.3390/math10193608.

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The past decade has seen an increase in the number of satellites in orbit and in highly dynamic satellite requests, making the control by ground stations inefficient. The traditional management composed of ground planning with separate onboard execution is seriously lagging in response to dynamically incoming tasks. To meet the demand for the real-time response to emergent events, a multi-autonomous-satellite system with a central-distributed collaborative architecture was formulated by an integer programming model. Based on the structure, evolutionary rules were proposed to solve this problem by the use of sequence solution construction and a constructed heuristic method based on gene expression programming evolution. First, the features of the problem are extracted based on domain knowledge, then, the problem-solving rules are evolved by gene expression programming. The simulation results reflect that the evolutionary rule completely surpasses the three types of heuristic rules with adaptive mechanisms and achieves a solution effect close to meta-heuristic algorithms with a reasonably fast solving speed.

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Hodan, David, Vojtech Mrazek i Zdenek Vasicek. "Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design". Genetic Programming and Evolvable Machines 22, nr 4 (2.10.2021): 539–72. http://dx.doi.org/10.1007/s10710-021-09416-6.

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Li, Yarong. "Sequence Alignment with Q-Learning Based on the Actor-Critic Model". ACM Transactions on Asian and Low-Resource Language Information Processing 20, nr 5 (2.07.2021): 1–7. http://dx.doi.org/10.1145/3433540.

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Multiple sequence alignment methods refer to a series of algorithmic solutions for the alignment of evolutionary-related sequences while taking into account evolutionary events such as mutations, insertions, deletions, and rearrangements under certain conditions. In this article, we propose a method with Q-learning based on the Actor-Critic model for sequence alignment. We transform the sequence alignment problem into an agent's autonomous learning process. In this process, the reward of the possible next action taken is calculated, and the cumulative reward of the entire process is calculated. The results show that the method we propose is better than the gene algorithm and the dynamic programming method.

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Artykuły w czasopismach na temat „Evolutionary programming (computer science)”

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