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Journal articles on the topic 'Evolutionary Algorithms'

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Published: 4 June 2021
Last updated: 16 July 2024

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1

Khera, Vansh. "Comparative Study of Evolutionary Algorithms." International Journal of Science and Research (IJSR) 12, no. 6 (June 5, 2023): 836–40. http://dx.doi.org/10.21275/sr23610122607.

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2

Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (June 2001): 127–46. http://dx.doi.org/10.1162/106365601750190370.

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Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.
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Bäck, Thomas. "Evolutionary algorithms." ACM SIGBIO Newsletter 12, no. 2 (June 1992): 26–31. http://dx.doi.org/10.1145/130686.130691.

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4

Graña, Manuel. "Evolutionary algorithms." Information Sciences 133, no. 3-4 (April 2001): 101–2. http://dx.doi.org/10.1016/s0020-0255(01)00079-2.

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5

Bartz-Beielstein, Thomas, Jürgen Branke, Jörn Mehnen, and Olaf Mersmann. "Evolutionary Algorithms." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4, no. 3 (April 24, 2014): 178–95. http://dx.doi.org/10.1002/widm.1124.

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6

Dioşan, Laura, and Mihai Oltean. "Evolutionary design of Evolutionary Algorithms." Genetic Programming and Evolvable Machines 10, no. 3 (March 20, 2009): 263–306. http://dx.doi.org/10.1007/s10710-009-9081-6.

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7

Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (May 30, 2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.
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Li, Kangshun, Fahui Gu, Wei Li, and Ying Huang. "A Dual-Population Evolutionary Algorithm Adapting to Complementary Evolutionary Strategy." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 01 (October 11, 2018): 1959004. http://dx.doi.org/10.1142/s0218001419590043.

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Optimization problems widely exist in scientific research and engineering practice, which have been one of the research hotshots and difficulties in intelligent computing. The single swarm intelligence optimization algorithms often show such defects as searching stagnation, low accuracy of convergence, part optimum and poor generalization ability when facing the increasingly sophisticated optimization problems. In the study of multiple population, the choice of evolution strategy often has great influence on the performance of the algorithm, and this paper puts forward a kind of dual-population evolutionary algorithm adapting to complementary evolutionary strategy (DPCEDT) based on the study of differential evolution algorithm, teaching and learning-based optimization algorithm. The simulation results show that the algorithm performs better than the TLBO-DE, HDT and DPDT and some other algorithms do in most test functions. It suggests that the complementary evolutionary strategies are more advantageous than other evolutionary strategies in dual-population evolutionary algorithms.
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Leciejewski, Sławomir, and Mariusz Szynkiewicz. "Algorithmicity of Evolutionary Algorithms." Studies in Logic, Grammar and Rhetoric 63, no. 1 (September 1, 2020): 87–100. http://dx.doi.org/10.2478/slgr-2020-0029.

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Abstract In the first part of our article we will refer the penetration of scientific terms into colloquial language, focusing on the sense in which the concept of an algorithm currently functions outside its original scope. The given examples will refer mostly to disciplines not directly related to computer science and to the colloquial language. In the next part we will also discuss the modifications made to the meaning of the term algorithm and how this concept is now understood in computer science. Finally, we will discuss the problem of algorithmicity of evolutionary algorithms, i.e. we will try to answer the question whether – and to what extent – they are still algorithms in the classical sense.
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10

Afathi, Maan. "Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems." Eastern-European Journal of Enterprise Technologies 6, no. 4 (114) (December 16, 2021): 6–14. http://dx.doi.org/10.15587/1729-4061.2021.245222.

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The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency
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Luan, Yuxuan, Junjiang He, Jingmin Yang, Xiaolong Lan, and Geying Yang. "Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning." International Journal of Intelligent Systems 2023 (November 10, 2023): 1–21. http://dx.doi.org/10.1155/2023/1666735.

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When solving real-world optimization problems, the uniformity of Pareto fronts is an essential strategy in multiobjective optimization problems (MOPs). However, it is a common challenge for many existing multiobjective optimization algorithms due to the skewed distribution of solutions and biases towards specific objective functions. This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address this limitation. Our algorithm utilizes uniform initialization and self-organizing map (SOM) to enhance population diversity and uniformity. We track the IGD value and use K-means and CNN refinement with crossover and mutation techniques during evolutionary stages. Our algorithm’s uniformity and objective function balance superiority were verified through comparative analysis with 13 other algorithms, including eight traditional multiobjective optimization algorithms, three machine learning-based enhanced multiobjective optimization algorithms, and two algorithms with objective initialization improvements. Based on these comprehensive experiments, it has been proven that our algorithm outperforms other existing algorithms in these areas.
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12

Strasser, Shane, John Sheppard, Nathan Fortier, and Rollie Goodman. "Factored Evolutionary Algorithms." IEEE Transactions on Evolutionary Computation 21, no. 2 (April 2017): 281–93. http://dx.doi.org/10.1109/tevc.2016.2601922.

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13

Berlich, R., and M. Kunze. "Parallel evolutionary algorithms." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 502, no. 2-3 (April 2003): 467–70. http://dx.doi.org/10.1016/s0168-9002(03)00471-6.

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14

Whitney, W., S. Rana, J. Dzubera, and K. E. Mathias. "Evaluating evolutionary algorithms." Artificial Intelligence 84, no. 1-2 (July 1996): 357–58. http://dx.doi.org/10.1016/0004-3702(96)81371-3.

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15

Whitley, Darrell, Soraya Rana, John Dzubera, and Keith E. Mathias. "Evaluating evolutionary algorithms." Artificial Intelligence 85, no. 1-2 (August 1996): 245–76. http://dx.doi.org/10.1016/0004-3702(95)00124-7.

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16

Chatain, Peter, Rocky Garg, and Lauren Tompkins. "Evolutionary Algorithms for Tracking Algorithm Parameter Optimization." EPJ Web of Conferences 251 (2021): 03071. http://dx.doi.org/10.1051/epjconf/202125103071.

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The reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. The most widely used and best performing tracking algorithms require significant geometry-specific tuning of the algorithm parameters to achieve best results. In this paper, we demonstrate the usage of machine learning techniques, particularly evolutionary algorithms, to find high performing configurations for the first step of tracking, called track seeding. We use a track seeding algorithm from the software framework A Common Tracking Software (ACTS). ACTS aims to provide an experimentindependent and framework-independent tracking software designed for modern computing architectures. We show that our optimization algorithms find highly performing configurations in ACTS without hand-tuning. These techniques can be applied to other reconstruction tasks, improving performance and reducing the need for laborious hand-tuning of parameters.
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17

Tran Binh Minh, Nguyen Long, and Thai Trung Kien. "An adaptive reference point technique to improve the quality of decomposition based multi-objective evolutionary algorithm." Journal of Military Science and Technology, CSCE7 (December 30, 2023): 3–14. http://dx.doi.org/10.54939/1859-1043.j.mst.csce7.2023.3-14.

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Applying multi-objective evolutionary optimization algorithms in solving multi-objective optimization problems is a research field that has received attention recently. In the literature of this research field, many studies have been carried out to propose multi-objective evolutionary algorithms or improve published algorithms. However, balancing the exploitation and exploration capabilities of the algorithm during the evolution process is still challenging. This article proposes an approach to solve that equilibrium problem based on analyzing population distribution during the evolutionary process to identify empty regions in which no solutions are selected. After that, information about empty regions with the most significant area will be combined with the current reference point to create a new reference point to prioritize choosing solutions in those regions. Experiments on 10 test problems of 2 typical benchmark sets showed that this mechanism increases the diversity of the population, thereby contributing to a balance between the algorithm's abilities in the evolutionary process and enhancing the algorithm.
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18

Ling, Sai Ho, and Hak Keung Lam. "Evolutionary Algorithms in Health Technologies." Algorithms 12, no. 10 (September 24, 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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19

Jiang, Dazhi, and Zhun Fan. "The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/474805.

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At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.
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20

Cui, Feng-Zhe, Zhi-Zheng Xu, Xiu-Kun Wang, Chong-Quan Zhong, and Hong-Fei Teng. "A dual-system cooperative co-evolutionary algorithm for satellite equipment layout optimization." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 13 (June 23, 2017): 2432–57. http://dx.doi.org/10.1177/0954410017715280.

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This paper develops a new dual-system cooperative co-evolutionary algorithm for multi-modules (or multi-bearing plate) satellite equipment layout optimization problem, based upon the Potter’s cooperative co-evolutionary framework. Firstly, a new dual-system framework based on the Potter’s cooperative co-evolutionary is constructed and then, corresponding system decomposition rule, matrix analysis method and coordination mechanism are presented. Finally, the way of matching algorithms (e.g. evolutionary algorithms and swarm intelligence algorithms) with systems A and B in the dual system is presented. The purpose is to enhance the computational accuracy and robustness of the developed algorithm for satellite equipment layout optimization problem. The experimental results show that the developed algorithm has better computational accuracy and robustness (computational success ratio and standard deviation) as compared with four dual-system algorithms and two single-system algorithms based upon Potter’s cooperative co-evolutionary.
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21

Meri, K., M. G. Arenas, A. M. Mora, J. J. Merelo, P. A. Castillo, P. García-Sánchez, and J. L. J. Laredo. "Cloud-based evolutionary algorithms: An algorithmic study." Natural Computing 12, no. 2 (November 17, 2012): 135–47. http://dx.doi.org/10.1007/s11047-012-9358-1.

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22

Hashem, M. M. A., Keigo Watanabe, and Kiyotaka Izumi. "Stable-Optimum Gain Tuning for Designing Mobile Robot Controllers Using Incest Prevented Evolution." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 5 (October 20, 1998): 164–75. http://dx.doi.org/10.20965/jaciii.1998.p0164.

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We present an evolution strategy (ES) algorithm - incest prevented evolution strategy (IPES) enhancing our novel evolution strategy (NES) algorithm. Validity of NES and IPES algorithms is compared with other evolutionary algorithms (EAs) and relative performances and also compared with test function results. The IPES algorithm shows the highest balance between exploration and exploitation over the NES algorithm on these test functions by achieving high-precision global results. Both algorithms are applied to solve stabilizing optimum gain tuning problems in mobile robot controllers. Two optimal servocontrollers are considered for a mobile robot with two independent drive wheels. A bidirectional fitness (cost) function is constructed for these controllers so that stable but optimum gains are tuned automatically evolutionarily instead of using a traditional algebraic Riccati equation solution. Two trajectory tracking control examples (straight line and circular) are considered for controllers. The superiority of the IPES algorithm over the NES algorithm is repeated in the application domain and the effectiveness of evolutionary gain tuning demonstrated by simulation results.
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23

Gottlieb, Jens, Elena Marchiori, and Claudio Rossi. "Evolutionary Algorithms for the Satisfiability Problem." Evolutionary Computation 10, no. 1 (March 2002): 35–50. http://dx.doi.org/10.1162/106365602317301763.

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Several evolutionary algorithms have been proposed for the satisfiability problem. We review the solution representations suggested in literature and choose the most promising one the bit string representation for further evaluation. An empirical comparison on commonly used benchmarks is presented for the most successful evolutionary algorithms and for WSAT, a prominent local search algorithm for the satisfi-ability problem. The key features of successful evolutionary algorithms are identified, thereby providing useful methodological guidelines for designing new heuristics. Our results indicate that evolutionary algorithms are competitive to WSAT.
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Abbas, Basim K. "Genetic Algorithms for Quadratic Equations." Aug-Sept 2023, no. 35 (August 26, 2023): 36–42. http://dx.doi.org/10.55529/jecnam.35.36.42.

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A common technique for finding accurate solutions to quadratic equations is to employ genetic algorithms. The authors propose using a genetic algorithm to find the complex roots of a quadratic problem. The technique begins by generating a collection of viable solutions, then proceeds to assess the suitability of each solution, choose parents for the next generation, and apply crossover and mutation to the offspring. For a predetermined number of generations, the process is repeated. Comparing the evolutionary algorithm's output to the quadratic formula proves its validity and uniqueness. Furthermore, the utility of the evolutionary algorithm has been demonstrated by programming it in Python code and comparing the outcomes to conventional intuitions.
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25

Zhao, Yan Wei, J. L. Zhang, and D. J. Peng. "Open Vehicle Routing Problem Using Quantum Evolutionary Algorithm." Advanced Materials Research 102-104 (March 2010): 807–12. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.807.

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Open vehicle routing problem is a kind of special vehicle routing problem, in which the vehicles do not return the depots after completing the task. Aiming at open vehicle routing problem, the mathematical model was founded by introducing virtual depots. A quantum evolutionary algorithm combined with local optimization algorithms was proposed in this paper, in which 0-1 matrix encoding was used to construct chromosomes, rotation gate with adaptively adjusting rotation angle was used to realize evolution, nearest neighbors and 2-Opt were incorporated to further improve solutions. Based on benchmark problems, the algorithm’s parameters were discussed, and the computation result was compared to those of other algorithms. The Computation results indicated that the proposed algorithm was an efficient method for solving open vehicle routing problem.
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26

Barros, Rodrigo C., Márcio P. Basgalupp, André C. P. L. F. de Carvalho, and Alex A. Freitas. "Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms." Evolutionary Computation 21, no. 4 (November 2013): 659–84. http://dx.doi.org/10.1162/evco_a_00101.

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This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.
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27

Civicioglu, P., U. H. Atasever, C. Ozkan, E. Besdok, A. E. Karkinli, and A. Kesikoglu. "Performance Comparison Of Evolutionary Algorithms For Image Clustering." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 71–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-71-2014.

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Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.
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28

Chernov, Ivan E., and Andrey V. Kurov. "APPLICATION OF GENETIC ALGORITHMS IN CRYPTOGRAPHY." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 1 (2022): 63–82. http://dx.doi.org/10.28995/2686-679x-2022-1-63-82.

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Currently in the development of computer technologies that ensure information security and information protection, cryptographic methods of protection are widely used. The main tasks in cryptography are the development of new encryption features, difficult to break and repetitive ciphers. To solve that problem, falling into the class of NP-complete ones, algorithms based on natural principles have been used in recent years. These include genetic algorithms (GA), evolutionary methods, swarm intelligence algorithms. In models and algorithms of evolutionary computations, the construction of basic models and rules is implemented, according to which it can change (evolve). In recent years, evolutionary computing schemes have been proposed, including the genetic algorithm, genetic programming, evolutionary programming, and evolutionary strategies. The paper discusses the existing cryptography methods, basic concepts and methods of modern cryptography, the notion of a genetic algorithm, a universal hash function, as well as a hash detection method and a genetic hashing algorithm built on it. A genetic algorithm was implemented in the Golang language, modified for the current problem of finding the optimal hash functions. A detailed description of each stage of the algorithm execution is given. Also, within the framework of the research, a study of the function of the genetic algorithm itself and the genetic hashing algorithm was carried out, evaluating the convergence of the genetic algorithm depending on the input data, and evaluating the possible direction of further research.
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Zhang, Rui, Zhiteng Wang, and Hongjun Zhang. "Quantum-Inspired Evolutionary Algorithm for Continuous Space Optimization Based on Multiple Chains Encoding Method of Quantum Bits." Mathematical Problems in Engineering 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/620325.

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This study proposes a novel quantum evolutionary algorithm called four-chain quantum-inspired evolutionary algorithm (FCQIEA) based on the four gene chains encoding method. In FCQIEA, a chromosome comprises four gene chains to expand the search space effectively and promote the evolutionary rate. Different parameters, including rotational angle and mutation probability, have been analyzed for better optimization. Performance comparison with other quantum-inspired evolutionary algorithms (QIEAs), evolutionary algorithms, and different chains of QIEA demonstrates the effectiveness and efficiency of FCQIEA.
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Ivkovic, Nikola, Domagoj Jakobovic, and Marin Golub. "Measuring Performance of Optimization Algorithms in Evolutionary Computation." International Journal of Machine Learning and Computing 6, no. 3 (June 2016): 167–71. http://dx.doi.org/10.18178/ijmlc.2016.6.3.593.

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31

Łapa, Krystian, Krzysztof Cpałka, Łukasz Laskowski, Andrzej Cader, and Zhigang Zeng. "Evolutionary Algorithm with a Configurable Search Mechanism." Journal of Artificial Intelligence and Soft Computing Research 10, no. 3 (July 1, 2020): 151–71. http://dx.doi.org/10.2478/jaiscr-2020-0011.

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AbstractIn this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, different strategies for mutating those parts of individuals that encode the used search operators are also taken into account. The effectiveness of the proposed algorithm has been tested using typical benchmarks used to test evolutionary algorithms.
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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 (August 9, 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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Sikora, Tomasz, and Wanda Gryglewicz-Kacerka. "APPLICATION OF GENETIC ALGORITHMS TO THE TRAVELING SALESMAN PROBLEM." Applied Computer Science 19, no. 2 (June 30, 2023): 55–62. http://dx.doi.org/10.35784/acs-2023-14.

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The purpose of this paper was to investigate in practice the possibility of using evolutionary algorithms to solve the traveling salesman problem on a real example. The goal was achieved by developing an original implementation of the evolutionary algorithm in Python, and by preparing an example of the traveling salesman problem in the form of a directed graph representing polish voivodship cities. As part of the work an application in Python was written. It provides a user interface which allows setting selected parameters of the evolutionary algorithm and solving the prepared problem. The results are presented in both text and graphical form. The correctness of the evolutionary algorithm's operation and the implementation was confirmed by performed tests. A large number of tested solutions (2500) and the analysis of the obtained results allowed for a conclusion that an optimal (relatively suboptimal) solution had been found.
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34

Schwehr, Peter. "Evolutionary Algorithms In Architecture." Open House International 36, no. 1 (March 1, 2011): 16–24. http://dx.doi.org/10.1108/ohi-01-2011-b0003.

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Change is a reliable constant. Constant change calls for strategies in managing everyday life and a high level of flexibility. Architecture must also rise to this challenge. The architect Richard Buckminster Fuller claimed that “A room should not be fixed, should not create a static mood, but should lend itself to change so that its occupants may play upon it as they would upon a piano (Krausse 2001).” This liberal interpretation in architecture defines the ability of a building to react to (ever-) changing requirements. The aim of the project is to investigate the flexibility of buildings using evolutionary algorithms characterized by Darwin. As a working model for development, the evolutionary algorithm consists of variation, selection and reproduction (VSR algorithm). The result of a VSR algorithm is adaptability (Buskes 2008). If this working model is applied to architecture, it is possible to examine as to what extent the adaptability of buildings – as an expression of a cultural achievement – is subject to evolutionary principles, and in which area the model seems unsuitable for the 'open buildings' criteria. (N. John Habraken). It illustrates the significance of variation, selection and replication in architecture and how evolutionary principles can be transferred to the issues of flexible buildings. What are the consequences for the building if it were to be designed and built with the help of evolutionary principles? How can we react to the growing demand for flexibilization of buildings by using evolutionary principles?
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35

Elhossini, Ahmed, Shawki Areibi, and Robert Dony. "Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization." Evolutionary Computation 18, no. 1 (March 2010): 127–56. http://dx.doi.org/10.1162/evco.2010.18.1.18105.

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This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
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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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Wu, Qinghua, Bin Wu, Chengyu Hu, and Xuesong Yan. "Evolutionary Multilabel Classification Algorithm Based on Cultural Algorithm." Symmetry 13, no. 2 (February 16, 2021): 322. http://dx.doi.org/10.3390/sym13020322.

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As one of the common methods to construct classifiers, naïve Bayes has become one of the most popular classification methods because of its solid theoretical basis, strong prior knowledge learning characteristics, unique knowledge expression forms, and high classification accuracy. This classification method has a symmetry phenomenon in the process of data classification. Although the naïve Bayes classifier has high classification performance in single-label classification problems, it is worth studying whether the multilabel classification problem is still valid. In this paper, with the naïve Bayes classifier as the basic research object, in view of the naïve Bayes classification algorithm’s shortage of conditional independence assumptions and label class selection strategies, the characteristics of weighted naïve Bayes is given a better label classifier algorithm framework; the introduction of cultural algorithms to search for and determine the optimal weights is proposed as the weighted naïve Bayes multilabel classification algorithm. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in classification performance.
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Misiak, Marcin. "Evolutionary Algorithms in Astrodynamics." International Journal of Astronomy and Astrophysics 06, no. 04 (2016): 435–39. http://dx.doi.org/10.4236/ijaa.2016.64035.

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WANG, Yong, Zi-Xing CAI, Yu-Ren ZHOU, and Chi-Xin XIAO. "Constrained Optimization Evolutionary Algorithms." Journal of Software 20, no. 1 (April 7, 2009): 11–29. http://dx.doi.org/10.3724/sp.j.1001.2009.00011.

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Yu, Xinjie, and Mitsuo Gen. "Introduction to Evolutionary Algorithms." Industrial Engineering and Management Systems 9, no. 4 (December 1, 2010): 348–49. http://dx.doi.org/10.7232/iems.2010.9.4.348.

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Alba, E., and M. Tomassini. "Parallelism and evolutionary algorithms." IEEE Transactions on Evolutionary Computation 6, no. 5 (October 2002): 443–62. http://dx.doi.org/10.1109/tevc.2002.800880.

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42

Bryden, K. M., D. A. Ashlock, S. Corns, and S. J. Willson. "Graph-based evolutionary algorithms." IEEE Transactions on Evolutionary Computation 10, no. 5 (October 2006): 550–67. http://dx.doi.org/10.1109/tevc.2005.863128.

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Kim, Jong-Han, and Min-Jea Tahk. "Accelerated Co-evolutionary Algorithms." International Journal of Aeronautical and Space Sciences 3, no. 1 (May 30, 2002): 50–60. http://dx.doi.org/10.5139/ijass.2002.3.1.050.

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Li, Bingdong, Jinlong Li, Ke Tang, and Xin Yao. "Many-Objective Evolutionary Algorithms." ACM Computing Surveys 48, no. 1 (September 29, 2015): 1–35. http://dx.doi.org/10.1145/2792984.

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Wang, Paul P. "Frontiers in Evolutionary Algorithms." Information Sciences 122, no. 2-4 (February 2000): 91. http://dx.doi.org/10.1016/s0020-0255(99)00117-6.

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García-Sánchez, P., J. González, P. A. Castillo, M. G. Arenas, and J. J. Merelo-Guervós. "Service oriented evolutionary algorithms." Soft Computing 17, no. 6 (February 6, 2013): 1059–75. http://dx.doi.org/10.1007/s00500-013-0999-5.

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Ombach, Jerzy. "Stability of evolutionary algorithms." Journal of Mathematical Analysis and Applications 342, no. 1 (June 2008): 326–33. http://dx.doi.org/10.1016/j.jmaa.2007.12.006.

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Preux, P., and E. G. Talbi. "Towards hybrid evolutionary algorithms." International Transactions in Operational Research 6, no. 6 (November 1999): 557–70. http://dx.doi.org/10.1111/j.1475-3995.1999.tb00173.x.

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Poli, Riccardo, and William B. Langdon. "Backward-chaining evolutionary algorithms." Artificial Intelligence 170, no. 11 (August 2006): 953–82. http://dx.doi.org/10.1016/j.artint.2006.04.003.

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Jing-wei, Huang, and Wei Wen-fang. "Evolutionary graph drawing algorithms." Wuhan University Journal of Natural Sciences 8, no. 1 (March 2003): 212–16. http://dx.doi.org/10.1007/bf02899481.

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