Abstract
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods. For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation. We also consider open issues and challenges that should be addressed in future work.
- Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. 1993. Mining association rules between sets of items in large databases. In ACM SIGMOD Record, Vol. 22. ACM, 207–216. Google ScholarDigital Library
- Rakesh Agrawal, Ramakrishnan Srikant, et al. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Vol. 1215. 487–499. Google ScholarDigital Library
- Harith Al-Sahaf, Ying Bi, Qi Chen, Andrew Lensen, Yi Mei, Yanan Sun, Binh Tran, Bing Xue, and Mengjie Zhang. 2019. A survey on evolutionary machine learning. J. Roy. Soc. New Zeal. 49, 2 (2019), 205–228.Google ScholarCross Ref
- Shafiq Alam, Gillian Dobbie, Yun Sing Koh, Patricia Riddle, and Saeed Ur Rehman. 2014. Research on particle swarm optimization based clustering: A systematic review of literature and techniques. Swarm Evolut. Comput. 17 (2014), 1–13.Google ScholarCross Ref
- Wissam A. Albukhanajer, Yaochu Jin, and Johann A. Briffa. 2017. Classifier ensembles for image identification using multi-objective Pareto features. Neurocomputing 238 (2017), 316–327. Google ScholarDigital Library
- Hamid Ali, Waseem Shahzad, and Farrukh Aslam Khan. 2012. Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization. Appl. Soft Comput. 12, 7 (2012), 1913–1928. Google ScholarDigital Library
- Ibrahim Aljarah, Majdi Mafarja, Ali Asghar Heidari, Hossam Faris, and Seyedali Mirjalili. 2019. Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowl. Inf. Syst. 62, 2 (2019), 1–33.Google Scholar
- Abdulaziz Almalaq and Jun Jason Zhang. 2018. Evolutionary deep learning-based energy consumption prediction for buildings. IEEE Access 7 (2018), 1520–1531.Google ScholarCross Ref
- Mehrdad Almasi and Mohammad Saniee Abadeh. 2015. Rare-PEARs: A new multi objective evolutionary algorithm to mine rare and non-redundant quantitative association rules. Knowl.-based Syst. 89 (2015), 366–384. Google ScholarDigital Library
- Akram AlSukker, Rami Khushaba, and Ahmed Al-Ani. 2010. Optimizing the k-nn metric weights using differential evolution. In Proceedings of the International Conference on Multimedia Computing and Information Technology (MCIT). IEEE, 89–92.Google ScholarCross Ref
- Amazon. 2017. Amazon EC2 P3 Instances. Retrieved from https://aws.amazon.com/es/ec2/instance-types/p3.Google Scholar
- Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2017. A brief survey of deep reinforcement learning. arXiv preprint arXiv:1708.05866 (2017).Google Scholar
- Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2017. Deep reinforcement learning: A brief survey. IEEE Sig. Process. Mag. 34, 6 (2017), 26–38.Google ScholarCross Ref
- Ilhan Aydin, Mehmet Karakose, and Erhan Akin. 2011. A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Appl. Soft Comput. 11, 1 (2011), 120–129. Google ScholarDigital Library
- Bodrunnessa Badhon, Mir Md Jahangir Kabir, Shuxiang Xu, and Monika Kabir. 2019. A survey on association rule mining based on evolutionary algorithms. Int. J. Comput. Applic. 41, 1 (2019), 1–11.Google Scholar
- Alejandro Baldominos, Yago Saez, and Pedro Isasi. 2020. On the automated, evolutionary design of neural networks: past, present, and future. Neural Comput. Applic. 32, 2 (2020), 1–27.Google ScholarCross Ref
- Rodrigo Coelho Barros, Márcio Porto Basgalupp, Andre C. P. L. F. De Carvalho, and Alex A. Freitas. 2011. A survey of evolutionary algorithms for decision-tree induction. IEEE Trans. Syst., Man, Cyber., Part C (Applic. Rev.) 42, 3 (2011), 291–312. Google ScholarDigital Library
- James C. Bezdek, Srinivas Boggavarapu, Lawrence O. Hall, and Amine Bensaid. 1994. Genetic algorithm guided clustering. In Proceedings of the 1st IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence. IEEE, 34–39.Google ScholarCross Ref
- Urvesh Bhowan, Mark Johnston, and Mengjie Zhang. 2011. Developing new fitness functions in genetic programming for classification with unbalanced data. IEEE Trans. Syst., Man, Cyber., Part B (Cyber.) 42, 2 (2011), 406–421. Google ScholarDigital Library
- Cosimo Birtolo, Diego De Chiara, Simona Losito, Pierluigi Ritrovato, and Mario Veniero. 2013. Searching optimal product bundles by means of GA-based Engine and Market Basket Analysis. In Proceedings of the Joint IFSA World Congress and NAFIPS Meeting (IFSA/NAFIPS). IEEE, 448–453.Google ScholarCross Ref
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer. Google ScholarDigital Library
- Nimagna Biswas, Saurajit Chakraborty, Sankha Subhra Mullick, and Swagatam Das. 2018. A parameter independent fuzzy weighted k-nearest neighbor classifier. Pattern Recog. Lett. 101 (2018), 80–87.Google ScholarCross Ref
- Veronica Bolon-Canedo, Noelia Sanchez-Marono, and Amparo Alonso-Betanzos. 2011. Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset. Exp. Syst. Applic. 38, 5 (2011), 5947–5957. Google ScholarDigital Library
- Leo Breiman. 1996. Bagging predictors. Mach. Learn. 24, 2 (1996), 123–140. Google ScholarDigital Library
- Lam Thu Bui, Thi Thu Huong Dinh, et al. 2018. A novel evolutionary multi-objective ensemble learning approach for forecasting currency exchange rates. Data Knowl. Eng. 114 (2018), 40–66.Google ScholarCross Ref
- Andrés Camero, Jamal Toutouh, Daniel H. Stolfi, and Enrique Alba. 2018. Evolutionary deep learning for car park occupancy prediction in smart cities. In Proceedings of the International Conference on Learning and Intelligent Optimization. Springer, 386–401.Google Scholar
- José Ramón Cano, Francisco Herrera, and Manuel Lozano. 2005. Stratification for scaling up evolutionary prototype selection. Pattern Recog. Lett. 26, 7 (2005), 953–963. Google ScholarDigital Library
- Erick Cantú-Paz and Chandrika Kamath. 2000. Combining evolutionary algorithms with oblique decision trees to detect bent-double galaxies. In Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III, Vol. 4120. International Society for Optics and Photonics, 63–71.Google ScholarCross Ref
- P. A. Castillo, Maribel García Arenas, Juan J. Merelo, V. M. Rivas, and Gustavo Romero. 2006. Multiobjective optimization of ensembles of multilayer perceptrons for pattern classification. In Parallel Problem Solving from Nature-PPSN IX. Springer, 453–462. Google ScholarDigital Library
- Kingshuk Chakravarty, Diptesh Das, Aniruddha Sinha, and Amit Konar. 2013. Feature selection by differential evolution algorithm-a case study in personnel identification. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, 892–899.Google ScholarCross Ref
- Yusi Cheng, Qiming Li, et al. 2015. GA-based multi-level association rule mining approach for defect analysis in the construction industry. Autom. Construct. 51 (2015), 78–91.Google ScholarCross Ref
- Brian Cheung and Carl Sable. 2011. Hybrid evolution of convolutional networks. In Proceedings of the 10th International Conference on Machine Learning and Applications and Workshops, Vol. 1. IEEE, 293–297. Google ScholarDigital Library
- Thomas Cover and Peter Hart. 1967. Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13, 1 (1967), 21–27. Google ScholarDigital Library
- Giuseppe Cuccu, Matthew Luciw, Jürgen Schmidhuber, and Faustino Gomez. 2011. Intrinsically motivated neuroevolution for vision-based reinforcement learning. In Proceedings of the IEEE International Conference on Development and Learning (ICDL), Vol. 2. IEEE, 1–7.Google ScholarCross Ref
- Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret. 2015. Robots that can adapt like animals. Nature 521, 7553 (2015), 503–507.Google Scholar
- David M. Curry and Cihan H. Dagli. 2014. Computational complexity measures for many-objective optimization problems. Procedia Comput. Sci. 36 (2014), 185–191.Google ScholarCross Ref
- Sérgio Francisco Da Silva, Marcela Xavier Ribeiro, João do E. S. Batista Neto, Caetano Traina-Jr, and Agma J. M. Traina. 2011. Improving the ranking quality of medical image retrieval using a genetic feature selection method. Decis. Supp. Syst. 51, 4 (2011), 810–820. Google ScholarDigital Library
- Ashraf Darwish, Aboul Ella Hassanien, and Swagatam Das. 2020. A survey of swarm and evolutionary computing approaches for deep learning. Artif. Intell. Rev. 53, 3 (2020), 1767–1812.Google ScholarCross Ref
- Swagatam Das and Amit Konar. 2009. Automatic image pixel clustering with an improved differential evolution. Appl. Soft Comput. 9, 1 (2009), 226–236. Google ScholarDigital Library
- Emiro De la Hoz, Eduardo De La Hoz, Andrés Ortiz, Julio Ortega, and Antonio Martínez-Álvarez. 2014. Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps. Knowl.-based Syst. 71 (2014), 322–338. Google ScholarDigital Library
- Jeff Dean and U. Hölzle. 2017. Build and train machine learning models on our new Google Cloud TPUs. Retrieved from https://www.blog.google/topics/google-cloud/google-cloud-offer-tpus-machine-learning.Google Scholar
- Hongbin Dong, Yuxin Dong, Cheng Zhou, Guisheng Yin, and Wei Hou. 2009. A fuzzy clustering algorithm based on evolutionary programming. Exp. Syst. Applic. 36, 9 (2009), 11792–11800. Google ScholarDigital Library
- Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019. Neural architecture search: A survey. J. Mach. Learn. Res. 20, 55 (2019), 1–21. Google ScholarDigital Library
- Zhiwei Fu, Bruce L. Golden, Shreevardhan Lele, S. Raghavan, and Edward A. Wasil. 2003. A genetic algorithm-based approach for building accurate decision trees. INFORMS J. Comput. 15, 1 (2003), 3–22. Google ScholarDigital Library
- K. Y. Fung, C. K. Kwong, Kin Wai Michael Siu, and K. M. Yu. 2012. A multi-objective genetic algorithm approach to rule mining for affective product design. Exp. Syst. Applic. 39, 8 (2012), 7411–7419. Google ScholarDigital Library
- Mikel Galar, Alberto Fernández, Edurne Barrenechea, and Francisco Herrera. 2013. EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recog. 46, 12 (2013), 3460–3471. Google ScholarDigital Library
- Salvador Garcı, Isaac Triguero, Cristobal J. Carmona, Francisco Herrera, et al. 2012. Evolutionary-based selection of generalized instances for imbalanced classification. Knowl.-based Syst. 25, 1 (2012), 3–12. Google ScholarDigital Library
- Javier Garcıa and Fernando Fernández. 2015. A comprehensive survey on safe reinforcement learning. J. Mach. Learn. Res. 16, 1 (2015), 1437–1480. Google ScholarDigital Library
- Salvador García, Alberto Fernández, and Francisco Herrera. 2009. Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems. Appl. Soft Comput. 9, 4 (2009), 1304–1314. Google ScholarDigital Library
- Salvador García and Francisco Herrera. 2009. Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy. Evolut. Comput. 17, 3 (2009), 275–306. Google ScholarDigital Library
- Fred Glover. 1986. Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13, 5 (1986), 533–549. Google ScholarDigital Library
- Taciana A. F. Gomes, Ricardo B. C. Prudêncio, Carlos Soares, André L. D. Rossi, and André Carvalho. 2012. Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing 75, 1 (2012), 3–13. Google ScholarDigital Library
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press. Google ScholarDigital Library
- Frédéric Gruau. 1994. Automatic definition of modular neural networks. Adapt. Behav. 3, 2 (1994), 151–183. Google ScholarDigital Library
- Shenkai Gu and Yaochu Jin. 2014. Generating diverse and accurate classifier ensembles using multi-objective optimization. In Proceedings of the IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM). IEEE, 9–15.Google ScholarCross Ref
- X. C. Guo, J. H. Yang, C. G. Wu, C. Y. Wang, and Y. C. Liang. 2008. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization. Neurocomputing 71, 16–18 (2008), 3211–3215. Google ScholarDigital Library
- Jiawei Han, Jian Pei, and Yiwen Yin. 2000. Mining frequent patterns without candidate generation. In ACM Sigmod Record, Vol. 29. ACM, 1–12. Google ScholarDigital Library
- Emrah Hancer, Bing Xue, Dervis Karaboga, and Mengjie Zhang. 2015. A binary ABC algorithm based on advanced similarity scheme for feature selection. Appl. Soft Comput. 36 (2015), 334–348. Google ScholarDigital Library
- Julia Handl and Bernd Meyer. 2002. Improved ant-based clustering and sorting in a document retrieval interface. In Proceedings of the International Conference on Parallel Problem Solving from Nature. Springer, 913–923. Google ScholarDigital Library
- Shigeru Haruyama and Qiangfu Zhao. 2002. Designing smaller decision trees using multiple objective optimization based GPS. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 6. IEEE.Google ScholarCross Ref
- Mohamed Jafar Abul Hasan and Sivakumar Ramakrishnan. 2011. A survey: Hybrid evolutionary algorithms for cluster analysis. Artif. Intell. Rev. 36, 3 (2011), 179–204. Google ScholarDigital Library
- Majeed Heydari and Amir Yousefli. 2017. A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach. Manag. Market. Chall. Knowl. Soc. 12, 1 (2017), 1–11.Google Scholar
- John H. Holland. 1992. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press. Google ScholarDigital Library
- Eduardo Raul Hruschka, Ricardo J. G. B. Campello, Alex A. Freitas, et al. 2009. A survey of evolutionary algorithms for clustering. IEEE Trans. Syst., Man, Cyber., Part C (Applic. Rev.) 39, 2 (2009), 133–155. Google ScholarDigital Library
- Jian Huang, Xiaoguang Hu, and Fan Yang. 2011. Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker. Measurement 44, 6 (2011), 1018–1027.Google ScholarCross Ref
- Huimin Jiang, C. K. Kwong, W. Y. Park, and K. M. Yu. 2018. A multi-objective PSO approach of mining association rules for affective design based on online customer reviews. J. Eng. Des. 29, 7 (2018), 381–403.Google ScholarCross Ref
- Hua Jiang, Shenghe Yi, Jing Li, Fengqin Yang, and Xin Hu. 2010. Ant clustering algorithm with K-harmonic means clustering. Exp. Syst. Applic. 37, 12 (2010), 8679–8684. Google ScholarDigital Library
- Raja Jothi, Elena Zotenko, Asba Tasneem, and Teresa M. Przytycka. 2006. COCO-CL: Hierarchical clustering of homology relations based on evolutionary correlations. Bioinformatics 22, 7 (2006), 779–788. Google ScholarDigital Library
- Krzysztof Jurczuk, Marcin Czajkowski, and Marek Kretowski. 2017. Evolutionary induction of a decision tree for large-scale data: A GPU-based approach. Soft Comput. 21, 24 (2017), 7363–7379. Google ScholarDigital Library
- Dervis Karaboga, Bahriye Akay, and Celal Ozturk. 2007. Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In Proceedings of the International Conference on Modeling Decisions for Artificial Intelligence. Springer, 318–329. Google ScholarDigital Library
- Stephen Kelly, Wolfgang Banzhaf, and Cedric Gondro. 2021. Evolving hierarchical memory-prediction machines in multi-task reinforcement learning. arXiv preprint arXiv:2106.12659 (2021).Google Scholar
- Stephen Kelly and Malcolm I. Heywood. 2017. Emergent tangled graph representations for Atari game playing agents. In Proceedings of the European Conference on Genetic Programming. Springer, 64–79.Google Scholar
- Stephen Kelly and Malcolm I. Heywood. 2018. Emergent solutions to high-dimensional multitask reinforcement learning. Evolut. Comput. 26, 3 (2018), 347–380. Google ScholarDigital Library
- James Kennedy and Russell Eberhart. 1995. Particle swarm optimization. In Proceedings of the International Conference on Neural Networks, Vol. 4. IEEE, 1942–1948.Google ScholarCross Ref
- Shauharda Khadka and Kagan Tumer. 2018. Evolution-guided policy gradient in reinforcement learning. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 1188–1200. Google ScholarDigital Library
- Mujahid H. Khalifa, Marwa Ammar, Wael Ouarda, and Adel M. Alimi. 2017. Particle swarm optimization for deep learning of convolution neural network. In Proceedings of the Sudan Conference on Computer Science and Information Technology (SCCSIT). IEEE, 1–5.Google Scholar
- Salman H. Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A. Sohel, and Roberto Togneri. 2017. Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 29, 8 (2017), 3573–3587.Google Scholar
- Taghi M. Khoshgoftaar and Yi Liu. 2007. A multi-objective software quality classification model using genetic programming. IEEE Trans. Reliab. 56, 2 (2007), 237–245.Google ScholarCross Ref
- Scott Kirkpatrick, C. Daniel Gelatt, and Mario P. Vecchi. 1983. Optimization by simulated annealing. Science 220, 4598 (1983), 671–680.Google Scholar
- Hiroaki Kitano. 1990. Empirical studies on the speed of convergence of neural network training using genetic algorithms. In Proceedings of the AAAI Conference on Artificial Intelligence. 789–795. Google ScholarDigital Library
- Jens Kober, J. Andrew Bagnell, and Jan Peters. 2013. Reinforcement learning in robotics: A survey. Int. J. Robot. Res. 32, 11 (2013), 1238–1274. Google ScholarDigital Library
- Jan Koutník, Jürgen Schmidhuber, and Faustino Gomez. 2014. Evolving deep unsupervised convolutional networks for vision-based reinforcement learning. In Proceedings of the Conference on Genetic and Evolutionary Computation. 541–548. Google ScholarDigital Library
- Bartosz Krawczyk, Michał Woźniak, and Gerald Schaefer. 2014. Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl. Soft Comput. 14 (2014), 554–562. Google ScholarDigital Library
- D. Praveen Kumar, Tarachand Amgoth, and Chandra Sekhara Rao Annavarapu. 2019. Machine learning algorithms for wireless sensor networks: A survey. Inf. Fus. 49 (2019), 1–25.Google ScholarDigital Library
- Pardeep Kumar and Amit Kumar Singh. 2019. Efficient generation of association rules from numeric data using genetic algorithm for smart cities. In Security in Smart Cities: Models, Applications, and Challenges. Springer, 323–343.Google Scholar
- Chan-Sheng Kuo, Tzung-Pei Hong, and Chuen-Lung Chen. 2007. Applying genetic programming technique in classification trees. Soft Comput. 11, 12 (2007), 1165–1172. Google ScholarDigital Library
- R. J. Kuo and L. M. Lin. 2010. Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Decis. Supp. Syst. 49, 4 (2010), 451–462. Google ScholarDigital Library
- R. J. Kuo, S. Y. Lin, and C. W. Shih. 2007. Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan. Exp. Syst. Applic. 33, 3 (2007), 794–808. Google ScholarDigital Library
- R. J. Kuo, Y. J. Syu, Zhen-Yao Chen, and Fang-Chih Tien. 2012. Integration of particle swarm optimization and genetic algorithm for dynamic clustering. Inf. Sci. 195 (2012), 124–140. Google ScholarDigital Library
- Halina Kwaśnicka and Kajetan Świtalski. 2006. Discovery of association rules from medical data-classical and evolutionary approaches. Annales Universitatis Mariae Curie-Sklodowska, Sectio AI–Informatica 4, 1 (2006), 204–217.Google Scholar
- Yann A. LeCun, Léon Bottou, Genevieve B. Orr, and Klaus-Robert Müller. 2012. Efficient backprop. In Neural Networks: Tricks of the Trade. Springer, 9–48. Google ScholarDigital Library
- C. K. H. Lee, King Lun Choy, George T. S. Ho, and Cathy H. Y. Lam. 2016. A slippery genetic algorithm-based process mining system for achieving better quality assurance in the garment industry. Exp. Syst. Applic. 46 (2016), 236–248. Google ScholarDigital Library
- Bingdong Li, Jinlong Li, Ke Tang, and Xin Yao. 2015. Many-objective evolutionary algorithms: A survey. ACM Comput. Surv. 48, 1 (2015), 1–35. Google ScholarDigital Library
- Juan Li, Yuan-xiang Li, Sha-sha Tian, and Jie-lin Xia. 2019. An improved cuckoo search algorithm with self-adaptive knowledge learning. Neural Comput. Applic. 32, 16 (2019), 1–31.Google ScholarCross Ref
- Juan Li, Yuan-xiang Li, Sha-sha Tian, and Jie Zou. 2019. Dynamic cuckoo search algorithm based on Taguchi opposition-based search. Int. J. Bio-insp. Comput. 13, 1 (2019), 59–69. Google ScholarDigital Library
- Juan Li, Dan-dan Xiao, Hong Lei, Ting Zhang, and Tian Tian. 2020. Using cuckoo search algorithm with q-learning and genetic operation to solve the problem of logistics distribution center location. Mathematics 8, 2 (2020), 149.Google ScholarCross Ref
- Juan Li, Dan-dan Xiao, Ting Zhang, Chun Liu, Yuan-xiang Li, and Gai-ge Wang. 2021. Multi-swarm cuckoo search algorithm with q-learning model. Comput. J. 64, 1 (2021), 108–131.Google ScholarCross Ref
- Juan Li, Yuan-Hua Yang, Hong Lei, and Gai-Ge Wang. 2020. Solving logistics distribution center location with improved cuckoo search algorithm. Int. J. Comput. Intell. Syst. 14, 1 (2020), 676–692.Google ScholarCross Ref
- Wei Li and Gai-Ge Wang. 2021. Elephant herding optimization using dynamic topology and biogeography-based optimization based on learning for numerical optimization. Eng. Comput. (2021), 1–29.Google Scholar
- Wei Li, Gai-Ge Wang, and Amir H. Alavi. 2020. Learning-based elephant herding optimization algorithm for solving numerical optimization problems. Knowl.-based Syst. 195 (2020), 105675.Google Scholar
- Xianneng Li, Shingo Mabu, Huiyu Zhou, Kaoru Shimada, and Kotaro Hirasawa. 2010. Genetic network programming with estimation of distribution algorithms for class association rule mining in traffic prediction. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, 1–8.Google ScholarCross Ref
- Xiangju Li, Hong Zhao, and William Zhu. 2015. A cost sensitive decision tree algorithm with two adaptive mechanisms. Knowl.-based Syst. 88 (2015), 24–33. Google ScholarDigital Library
- Jason Liang, Elliot Meyerson, Babak Hodjat, Dan Fink, Karl Mutch, and Risto Miikkulainen. 2019. Evolutionary neural automl for deep learning. In Proceedings of the Genetic and Evolutionary Computation Conference. 401–409. Google ScholarDigital Library
- Amy H. L. Lim, Chien-Sing Lee, and Murali Raman. 2012. Hybrid genetic algorithm and association rules for mining workflow best practices. Exp. Syst. Applic. 39, 12 (2012), 10544–10551. Google ScholarDigital Library
- Pin Lim, Chi Keong Goh, and Kay Chen Tan. 2016. Evolutionary cluster-based synthetic oversampling ensemble (eco-ensemble) for imbalance learning. IEEE Trans. Cyber. 47, 9 (2016), 2850–2861.Google ScholarCross Ref
- Yongguo Liu, Kefei Chen, Xiaofeng Liao, and Wei Zhang. 2004. A genetic clustering method for intrusion detection. Pattern Recog. 37, 5 (2004), 927–942.Google ScholarCross Ref
- Stuart Lloyd. 1982. Least squares quantization in PCM. IEEE Trans. Inf. Theor. 28, 2 (1982), 129–137. Google ScholarDigital Library
- Ana Carolina Lorena and Andre C. P. L. F. De Carvalho. 2008. Evolutionary tuning of SVM parameter values in multiclass problems. Neurocomputing 71, 16–18 (2008), 3326–3334. Google ScholarDigital Library
- José Antonio Lozano and Pedro Larranaga. 1999. Applying genetic algorithms to search for the best hierarchical clustering of a dataset. Pattern Recog. Lett. 20, 9 (1999), 911–918. Google ScholarDigital Library
- Nannan Lu, Shingo Mabu, Tuo Wang, and Kotaro Hirasawa. 2013. An efficient class association rule-pruning method for unified intrusion detection system using genetic algorithm. IEEJ Trans. Electric. Electron. Eng. 8, 2 (2013), 164–172.Google ScholarCross Ref
- Zhichao Lu, Kalyanmoy Deb, Erik Goodman, Wolfgang Banzhaf, and Vishnu Naresh Boddeti. 2020. Nsganetv2: Evolutionary multi-objective surrogate-assisted neural architecture search. In Proceedings of the European Conference on Computer Vision. Springer, 35–51.Google ScholarDigital Library
- Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf. 2019. NSGA-Net: Neural architecture search using multi-objective genetic algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference. 419–427. Google ScholarDigital Library
- José María Luna, Cristóbal Romero, José Raúl Romero, and Sebastián Ventura. 2015. An evolutionary algorithm for the discovery of rare class association rules in learning management systems. Appl. Intell. 42, 3 (2015), 501–513. Google ScholarDigital Library
- Patrick C. H. Ma, Keith C. C. Chan, Xin Yao, and David K. Y. Chiu. 2006. An evolutionary clustering algorithm for gene expression microarray data analysis. IEEE Trans. Evolut. Comput. 10, 3 (2006), 296–314. Google ScholarDigital Library
- Sai Ma and Fulei Chu. 2019. Ensemble deep learning-based fault diagnosis of rotor bearing systems. Comput. Industr. 105 (2019), 143–152.Google ScholarCross Ref
- Shingo Mabu, Ci Chen, Nannan Lu, Kaoru Shimada, and Kotaro Hirasawa. 2010. An intrusion-detection model based on fuzzy class-association-rule mining using genetic network programming. IEEE Trans. Syst., Man, Cyber., Part C (Applic. Rev.) 41, 1 (2010), 130–139. Google ScholarDigital Library
- Arif Jamal Malik and Farrukh Aslam Khan. 2018. A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection. Cluster Comput. 21, 1 (2018), 667–680.Google ScholarCross Ref
- Veenu Mangat and Renu Vig. 2014. Novel associative classifier based on dynamic adaptive PSO: Application to determining candidates for thoracic surgery. Exp. Syst. Applic. 41, 18 (2014), 8234–8244.Google ScholarCross Ref
- Vittorio Maniezzo, Luca Maria Gambardella, and Fabio De Luigi. 2004. Ant colony optimization. In New Optimization Techniques in Engineering. Springer, 101–121.Google Scholar
- María Martínez-Ballesteros, Francisco Martínez-Álvarez, A. Troncoso, and José C. Riquelme. 2009. Quantitative association rules applied to climatological time series forecasting. In Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning. Springer, 284–291. Google ScholarDigital Library
- María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso, and José C Riquelme. 2011. An evolutionary algorithm to discover quantitative association rules in multidimensional time series. Soft Comput. 15, 10 (2011), 2065. Google ScholarDigital Library
- María Martínez-Ballesteros, A. Troncoso, Francisco Martínez-Álvarez, and José C. Riquelme. 2010. Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integr. Comput.-aided Eng. 17, 3 (2010), 227–242. Google ScholarDigital Library
- Jacinto Mata, José-Luis Alvarez, and José-Cristobal Riquelme. 2002. Discovering numeric association rules via evolutionary algorithm. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 40–51. Google ScholarDigital Library
- Daniel Mateos-García, Jorge García-Gutiérrez, and José C. Riquelme-Santos. 2016. An evolutionary voting for k-nearest neighbours. Exp. Syst. Applic. 43 (2016), 9–14. Google ScholarDigital Library
- Ron Meir and Gunnar Rätsch. 2003. An introduction to boosting and leveraging. In Advanced Lectures on Machine Learning. Springer, 118–183. Google ScholarDigital Library
- Jan Hendrik Metzen, Mark Edgington, Yohannes Kassahun, and Frank Kirchner. 2008. Analysis of an evolutionary reinforcement learning method in a multiagent domain. In Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 1. Citeseer, 291–298. Google ScholarDigital Library
- Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Daniel Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, et al. 2019. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing. Elsevier, 293–312.Google Scholar
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, et al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529.Google Scholar
- Anirban Mukhopadhyay, Ujjwal Maulik, and Sanghamitra Bandyopadhyay. 2015. A survey of multiobjective evolutionary clustering. ACM Comput. Surv. 47, 4 (2015), 1–46. Google ScholarDigital Library
- Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyopadhyay, and Carlos Artemio Coello Coello. 2013. A survey of multiobjective evolutionary algorithms for data mining: Part I. IEEE Trans. Evolut. Comput. 18, 1 (2013), 4–19.Google ScholarCross Ref
- Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyopadhyay, and Carlos A. Coello Coello. 2013. Survey of multiobjective evolutionary algorithms for data mining: Part II. IEEE Trans. Evolut. Comput. 18, 1 (2013), 20–35.Google ScholarCross Ref
- S. R. Nanda, Biswajit Mahanty, and M. K. Tiwari. 2010. Clustering Indian stock market data for portfolio management. Exp. Syst. Applic. 37, 12 (2010), 8793–8798. Google ScholarDigital Library
- Satyasai Jagannath Nanda and Ganapati Panda. 2014. A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evolut. Comput. 16 (2014), 1–18.Google ScholarCross Ref
- NVIDIA. 2017. The world's most efficient supercomputer for AI and deep learning. Retrieved from http://images.nvidia.com/content/pdf/infographic/dgx-saturnv-infographic.pdf.Google Scholar
- Luiz S. Oliveira, Robert Sabourin, Flávio Bortolozzi, and Ching Y. Suen. 2002. Feature selection using multi-objective genetic algorithms for handwritten digit recognition. In Object Recognition Supported by User Interaction for Service Robots, Vol. 1. IEEE, 568–571. Google ScholarDigital Library
- Aytuğ Onan, Serdar Korukoğlu, and Hasan Bulut. 2017. A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification. Inf. Process. Manag. 53, 4 (2017), 814–833. Google ScholarDigital Library
- Fernando E. B. Otero, Alex A. Freitas, and Colin G. Johnson. 2012. Inducing decision trees with an ant colony optimization algorithm. Appl. Soft Comput. 12, 11 (2012), 3615–3626. Google ScholarDigital Library
- Rafael S. Parpinelli and Heitor S. Lopes. 2011. New inspirations in swarm intelligence: A survey. Int. J. Bio-insp. Comput. 3, 1 (2011), 1–16. Google ScholarDigital Library
- Russel Pears and Yun Sing Koh. 2011. Weighted association rule mining using particle swarm optimization. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 327–338. Google ScholarDigital Library
- Abdolrahman Peimankar, Stephen John Weddell, Thahirah Jalal, and Andrew Craig Lapthorn. 2018. Multi-objective ensemble forecasting with an application to power transformers. Appl. Soft Comput. 68 (2018), 233–248.Google ScholarDigital Library
- Lu Peng, Shan Liu, Rui Liu, and Lin Wang. 2018. Effective long short-term memory with differential evolution algorithm for electricity price prediction. Energy 162 (2018), 1301–1314.Google ScholarCross Ref
- Pyari Mohan Pradhan and Ganapati Panda. 2012. Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making. Ad Hoc Netw. 10, 6 (2012), 1134–1145. Google ScholarDigital Library
- Esmat Rashedi, Elaheh Rashedi, and Hossein Nezamabadi-pour. 2018. A comprehensive survey on gravitational search algorithm. Swarm Evolut. Comput. 41 (2018), 141–158.Google ScholarCross Ref
- Esteban Real, Chen Liang, David So, and Quoc Le. 2020. AutoML-zero: Evolving machine learning algorithms from scratch. In Proceedings of the International Conference on Machine Learning. PMLR, 8007–8019.Google Scholar
- Victor Henrique Alves Ribeiro and Gilberto Reynoso-Meza. 2019. A holistic multi-objective optimization design procedure for ensemble member generation and selection. Appl. Soft Comput. 83 (2019), 105664.Google ScholarCross Ref
- Victor Henrique Alves Ribeiro and Gilberto Reynoso-Meza. 2020. Ensemble learning by means of a multi-objective optimization design approach for dealing with imbalanced data sets. Exp. Syst. Applic. 147 (2020), 113232.Google ScholarCross Ref
- Cristóbal Romero, Amelia Zafra, Jose María Luna, and Sebastián Ventura. 2013. Association rule mining using genetic programming to provide feedback to instructors from multiple-choice quiz data. Exp. Syst. 30, 2 (2013), 162–172.Google ScholarCross Ref
- Hussein Samma, Chee Peng Lim, and Junita Mohamad Saleh. 2016. A new reinforcement learning-based memetic particle swarm optimizer. Appl. Soft Comput. 43 (2016), 276–297. Google ScholarDigital Library
- Manish Sarkar, B. Yegnanarayana, and Deepak Khemani. 1997. A clustering algorithm using an evolutionary programming-based approach. Pattern Recog. Lett. 18, 10 (1997), 975–986. Google ScholarDigital Library
- Mansour Sheikhan and Maryam Sharifi Rad. 2013. Using particle swarm optimization in fuzzy association rules-based feature selection and fuzzy ARTMAP-based attack recognition. Secur. Commun. Netw. 6, 7 (2013), 797–811.Google ScholarCross Ref
- Christopher Smith and Yaochu Jin. 2014. Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction. Neurocomputing 143 (2014), 302–311. Google ScholarDigital Library
- Qin Song, Yu-Jun Zheng, Yu Xue, Wei-Guo Sheng, and Mei-Rong Zhao. 2017. An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination. Neurocomputing 226 (2017), 16–22. Google ScholarDigital Library
- Pedro Sousa, Paulo Cortez, Rui Vaz, Miguel Rocha, and Miguel Rio. 2013. Email spam detection: A symbiotic feature selection approach fostered by evolutionary computation. Int. J. Inf. Technol. Decis. Mak. 12, 04 (2013), 863–884.Google ScholarCross Ref
- Andreas Stafylopatis and Konstantinos Blekas. 1998. Autonomous vehicle navigation using evolutionary reinforcement learning. Eur. J. Oper. Res. 108, 2 (1998), 306–318.Google ScholarCross Ref
- Kenneth O. Stanley, Jeff Clune, Joel Lehman, and Risto Miikkulainen. 2019. Designing neural networks through neuroevolution. Nat. Mach. Intell. 1, 1 (2019), 24–35.Google ScholarCross Ref
- Kenneth O. Stanley, David B. D'Ambrosio, and Jason Gauci. 2009. A hypercube-based encoding for evolving large-scale neural networks. Artif. Life 15, 2 (2009), 185–212. Google ScholarDigital Library
- Kenneth O. Stanley and Risto Miikkulainen. 2002. Evolving neural networks through augmenting topologies. Evolut. Comput. 10, 2 (2002), 99–127. Google ScholarDigital Library
- Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, and Jeff Clune. 2017. Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:1712.06567 (2017).Google Scholar
- Thorsten Suttorp and Christian Igel. 2006. Multi-objective optimization of support vector machines. In Multi-objective Machine Learning. Springer, 199–220.Google Scholar
- Amirhessam Tahmassebi and Amir H. Gandomi. 2018. Building energy consumption forecast using multi-objective genetic programming. Measurement 118 (2018), 164–171.Google ScholarCross Ref
- Amirhessam Tahmassebi and Amir H. Gandomi. 2018. Genetic programming based on error decomposition: A big data approach. In Genetic Programming Theory and Practice XV. Springer, 135–147. Google ScholarDigital Library
- Amirhessam Tahmassebi, Amir H. Gandomi, and Anke Meyer-Baese. 2018. An evolutionary online framework for MOOC performance using EEG data. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE, 1–8.Google ScholarCross Ref
- Amirhessam Tahmassebi, Amir H. Gandomi, and Anke Meyer-Baese. 2018. A Pareto front based evolutionary model for airfoil self-noise prediction. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE, 1–8.Google ScholarCross Ref
- Amirhessam Tahmassebi, Behshad Mohebali, Anke Meyer-Baese, and Amir H. Gandomi. 2020. Multiobjective genetic programming for reinforced concrete beam modeling. Appl. AI Lett. 1, 1 (2020), e9.Google ScholarCross Ref
- Amirhessam Tahmassebi and Trace Smith. 2021. SlickML: Slick Machine Learning in Python. Retrieved from https://github.com/slickml/slick-ml.Google Scholar
- Pınar Tapkan, Lale Özbakır, Sinem Kulluk, and Adil Baykasoğlu. 2016. A cost-sensitive classification algorithm: BEE-Miner. Knowl.-based Syst. 95 (2016), 99–113. Google ScholarDigital Library
- Kshitij Tayal and Vadlamani Ravi. 2016. Particle swarm optimization trained class association rule mining: Application to phishing detection. In Proceedings of the International Conference on Informatics and Analytics. 1–8. Google ScholarDigital Library
- Akbar Telikani and Amir H. Gandomi. 2019. Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things. Internet of Things 14 (2019), 100122.Google ScholarCross Ref
- Akbar Telikani, Amir H. Gandomi, and Asadollah Shahbahrami. 2020. A survey of evolutionary computation for association rule mining. Inf. Sci. 524 (2020).Google Scholar
- Akbar Telikani and Asadollah Shahbahrami. 2018. Data sanitization in association rule mining: An analytical review. Exp. Syst. Applic. 96 (2018), 406–426. Google ScholarDigital Library
- Cuong To and Tuan D. Pham. 2009. Analysis of cardiac imaging data using decision tree based parallel genetic programming. In Proceedings of the 6th International Symposium on Image and Signal Processing and Analysis. IEEE, 317–320.Google Scholar
- Binh Tran, Bing Xue, and Mengjie Zhang. 2016. Genetic programming for feature construction and selection in classification on high-dimensional data. Mem. Comput. 8, 1 (2016), 3–15.Google ScholarCross Ref
- Isaac Triguero, Mikel Galar, Humberto Bustince, and Francisco Herrera. 2017. A first attempt on global evolutionary undersampling for imbalanced big data. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE, 2054–2061.Google ScholarCross Ref
- Isaac Triguero, Salvador García, and Francisco Herrera. 2011. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recog. 44, 4 (2011), 901–916. Google ScholarDigital Library
- Alan M. Turing. 1950. Computing machinery and intelligence. Mind 59, 236 (1950), 433–460.Google ScholarCross Ref
- Shweta Tyagi and Kamal K. Bharadwaj. 2013. Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining. Swarm Evolut. Comput. 13 (2013), 1–12.Google ScholarCross Ref
- Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg, Tim Verbelen, and Jan S. Rellermeyer. 2020. A survey on distributed machine learning. ACM Comput. Surv. 53, 2 (2020), 1–33. Google ScholarDigital Library
- Leandro D. Vignolo, Diego H. Milone, and Jacob Scharcanski. 2013. Feature selection for face recognition based on multi-objective evolutionary wrappers. Exp. Syst. Applic. 40, 13 (2013), 5077–5084.Google ScholarCross Ref
- Wengdong Wang and Susan M. Bridges. 2000. Genetic algorithm optimization of membership functions for mining fuzzy association rules. Depart. Comput. Sci. Mississ. State Univ. 2 (2000).Google Scholar
- Feng Wen, Guo Zhang, Lingfeng Sun, Xingqiao Wang, and Xiaowei Xu. 2019. A hybrid temporal association rules mining method for traffic congestion prediction. Comput. Industr. Eng. 130 (2019), 779–787.Google ScholarDigital Library
- Chun-Hui Wu, Ta-Cheng Chen, Yi-Chih Hsieh, and Huei-Ling Tsao. 2019. A hybrid rule mining approach for cardiovascular disease detection in traditional Chinese medicine. J. Intell. Fuzz. Syst. 36, 2 (2019), 861–870.Google ScholarCross Ref
- Bing Xue, Mengjie Zhang, and Will N. Browne. 2014. Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Appl. Soft Comput. 18 (2014), 261–276. Google ScholarDigital Library
- Bing Xue, Mengjie Zhang, Will N. Browne, and Xin Yao. 2015. A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evolut. Comput. 20, 4 (2015), 606–626.Google ScholarDigital Library
- Dongdong Yang, Licheng Jiao, Maoguo Gong, and Fang Liu. 2011. Artificial immune multi-objective SAR image segmentation with fused complementary features. Inf. Sci. 181, 13 (2011), 2797–2812. Google ScholarDigital Library
- Xin Yao. 1993. A review of evolutionary artificial neural networks. Int. J. Intell. Syst. 8, 4 (1993), 539–567.Google ScholarCross Ref
- Jianbo Yu, Lifeng Xi, and Shijin Wang. 2007. An improved particle swarm optimization for evolving feedforward artificial neural networks. Neural Process. Lett. 26, 3 (2007), 217–231. Google ScholarDigital Library
- Mohammed Javeed Zaki. 2000. Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12, 3 (2000), 372–390. Google ScholarDigital Library
- Maider Zamalloa, Germán Bordel, Luis Javier Rodríguez, and Mikel Peñagarikano. 2006. Feature selection based on genetic algorithms for speaker recognition. In Proceedings of the IEEE Odyssey-The Speaker and Language Recognition Workshop. IEEE, 1–8.Google ScholarCross Ref
- Chong Zhang, Kay Chen Tan, Haizhou Li, and Geok Soon Hong. 2018. A cost-sensitive deep belief network for imbalanced classification. IEEE Trans. Neural Netw. Learn. Syst. 30, 1 (2018), 109–122.Google ScholarCross Ref
- Lei Zhang, Guanglong Fu, Fan Cheng, Jianfeng Qiu, and Yansen Su. 2018. A multi-objective evolutionary approach for mining frequent and high utility itemsets. Appl. Soft Comput. 62 (2018), 974–986.Google ScholarCross Ref
- Zaifang Zhang, Nana Chai, Egon Ostrosi, and Yuliang Shang. 2019. Extraction of association rules in the schematic design of product service system based on Pareto-MODGDFA. Comput. Industr. Eng. 129 (2019), 392–403.Google ScholarCross Ref
- Changjiu Zhou. 2002. Robot learning with GA-based fuzzy reinforcement learning agents. Inf. Sci. 145, 1–2 (2002), 45–68.Google ScholarCross Ref
Index Terms
- Evolutionary Machine Learning: A Survey
Recommendations
Niching without niching parameters: particle swarm optimization using a ring topology
Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. These niching parameters, often used to inform a niching algorithm how far apart ...
A lvy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems
Display OmittedAn expanded framework of shuffled frog-leaping algorithm for continuous optimization problem is performed according to the mechanism of exploration and exploitation, in which a lvy flight-based attractor was proposed. Experimental results ...
Analysis of evolutionary multi-tasking as an island model
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference CompanionRecently, an idea of evolutionary multi-tasking has been proposed and applied to various types of optimization problems. The basic idea of evolutionary multi-tasking is to simultaneously solve multiple optimization problems (i.e., tasks) in a ...
Comments