{"type":"ScholarlyArticle","title":"A Group-based Approach to Improve Multifactorial Evolutionary Algorithm","authors":[{"name":"Jing Tang","slug":"jing-tang","given":"Jing","family":"Tang","affiliation":"Department of Computer Science & Information Systems , Teesside University , UK"},{"name":"Yingke Chen","slug":"yingke-chen","given":"Yingke","family":"Chen","affiliation":"Department of Computer Science & Information Systems , Teesside University , UK"},{"name":"Zixuan Deng","slug":"zixuan-deng","given":"Zixuan","family":"Deng","affiliation":"School of Computer Science & Engineering , University of Electronic Science and Technology of China , China"},{"name":"Yanping Xiang","slug":"yanping-xiang","given":"Yanping","family":"Xiang","affiliation":"School of Computer Science & Engineering , University of Electronic Science and Technology of China , China"},{"name":"Colin Paul Joy","slug":"colin-paul-joy","given":"Colin Paul","family":"Joy","affiliation":"Department of Computer Science & Information Systems , Teesside University , UK"}],"abstract":"Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based evolutionaryalgorithm and provides an efficient way to evolve individuals for solving multiple tasks concurrently.Its efficiency is derived by implicitly transferring the genetic information among tasks.However, MFEA doesn?t distinguish the information quality in the transfer compromising the algorithmperformance. We propose a group-based MFEA that groups tasks of similar types and selectivelytransfers the genetic information only within the groups. We also develop a new selection criterionand an additional mating selection mechanism in order to strengthen the effectiveness andefficiency of the improved MFEA. We conduct the experiments in both the cross-domain and intra-domainproblems.","doi":"10.24963/ijcai.2018/538","url":"https://ijcai.org/proceedings/2018/538","pdf":"https://ijcai.org/proceedings/2018/0538.pdf","markdown":"https://ijcai.org/proceedings/2018/538.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/538","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":538,"pages":{"first":3870,"last":3876},"keywords":["Multidisciplinary Topics and Applications: Autonomic Computing","Heuristic Search and Game Playing: Evaluation and Analysis"],"videos":[],"references":[{"position":1,"raw":"[Bäck et al., 1997] T Bäck, U Hammel, and H.-P. Schwefel. Evolutionary Computation: Comments on the History and Current State. IEEE Transactions on Evolutionary Com- putation, 1(1):3-17, 1997.","title":"Evolutionary Computation: Comments on the History and Current State","authors":"Bäck, H.-P Hammel, Schwefel","year":"1997","venue":"IEEE Transactions on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Caruana, 1997] Rich Caruana. Multitask learning. Machine Learning, 28(1):41-75, Jul 1997.","title":"Multitask learning","authors":"Caruana","year":"1997","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chauhan et al., 2013] Pinkey Chauhan, Kusum Deep, and Millie Pant. Novel inertia weight strategies for particle swarm optimization. Memetic Computing, 5(3):229-251, 2013.","title":"Novel inertia weight strategies for particle swarm optimization","authors":"Pinkey Chauhan, Kusum Deep, Millie Pant","year":"2013","venue":"Memetic Computing","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cheng et al., 2017] Mei-Ying Cheng, Abhishek Gupta, Yew-Soon Ong, and Zhi-Wei Ni. Coevolutionary multi- tasking for concurrent global optimization: With case s- tudies in complex engineering design. Engineering Appli- cations of Artificial Intelligence, 64:13-24, 2017.","title":"Coevolutionary multitasking for concurrent global optimization: With case studies in complex engineering design","authors":"Mei-Ying Cheng, Abhishek Gupta, Yew-Soon Ong, Zhi-Wei Ni","year":"2017","venue":"Engineering Applications of Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Coello, 2006] CA Coello Coello. Evolutionary multi- objective optimization: a historical view of the field. IEEE Computational Intelligence Magazine, 1(1):28-36, 2006.","title":"Evolutionary multiobjective optimization: a historical view of the field","authors":"; Ca Coello Coello, Coello","year":"2006","venue":"IEEE Computational Intelligence Magazine","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Črepinšek et al., 2013] Matej Črepinšek, Shih-Hsi Liu, and Marjan Mernik. Exploration and Exploitation in Evolu- tionary Algorithms. ACM Computing Surveys, 45(3):1-33, 2013.","title":"Exploration and Exploitation in Evolutionary Algorithms","authors":"Matej Črepinšek, Shih-Hsi Liu, Marjan Mernik","year":"2013","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Deb et al., 2000] Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Parallel Problem Solving from Nature PPSN VI, pages 849-858, 2000.","title":"A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II","authors":"Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, Meyarivan","year":"2000","venue":"Parallel Problem Solving from Nature PPSN VI","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Deb et al., 2002] Kalyanmoy Deb, Amrit Pratap, Sameer A- garwal, and T Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evo- lutionary Computation, 6(2):182-197, 2002.","title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","authors":"Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, Meyarivan","year":"2002","venue":"IEEE Transactions on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fonseca and Fleming, 1995] Carlos M Fonseca and Peter J Fleming. An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evolutionary Computation, 3(1):1-16, 1995.","title":"An Overview of Evolutionary Algorithms in Multiobjective Optimization","authors":"M Carlos, Peter J Fonseca, Fleming","year":"1995","venue":"Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Guo and Yang, 2015] Shu Mei Guo and Chin Chang Yang. Enhancing differential evolution utilizing eigenvector- based crossover operator. IEEE Transactions on Evolu- tionary Computation, 19(1):31-49, 2015.","title":"Enhancing differential evolution utilizing eigenvectorbased crossover operator","authors":"Shu Mei, Guo, Chin Chang, Yang","year":"2015","venue":"IEEE Transactions on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gupta and Ong, 2016] Abhishek Gupta and Yew-soon Ong. Genetic Transfer or Population Diversification? Decipher- ing the Secret Ingredients of Evolutionary Multitask Opti- mization. jul 2016.","title":"Abhishek Gupta and Yew-soon Ong. Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization","authors":"Ong Gupta","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gupta et al., 2016] Abhishek Gupta, Yew Soon Ong, and Liang Feng. Multifactorial Evolution: Toward Evolu- tionary Multitasking. IEEE Transactions on Evolutionary Computation, 20(3):343-357, 2016.","title":"Multifactorial Evolution: Toward Evolutionary Multitasking","authors":"Abhishek Gupta, Yew, Soon Ong, Liang Feng","year":"2016","venue":"IEEE Transactions on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ishibuchi et al., 2008] Hisao Ishibuchi, Kaname Narukawa, Noritaka Tsukamoto, and Yusuke Nojima. An empirical study on similarity-based mating for evolutionary multi- objective combinatorial optimization. European Journal of Operational Research, 188(1):57-75, 2008.","title":"An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization","authors":"Hisao Ishibuchi, Kaname Narukawa, Noritaka Tsukamoto, Yusuke Nojima","year":"2008","venue":"European Journal of Operational Research","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Le et al., 2009] Minh Nghia Le, Yew Soon Ong, Yaochu Jin, and Bernhard Sendhoff. Lamarckian memetic algo- rithms: Local optimum and connectivity structure analy- sis. Memetic Computing, 1(3):175-190, 2009.","title":"Lamarckian memetic algorithms: Local optimum and connectivity structure analysis","authors":"Le","year":"2009","venue":"Memetic Computing","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[López et al., 2010] Edgar Galván López, James Mcdermott, Michael O Neill, and Anthony Brabazon. Towards an Un- derstanding of Locality in Genetic Programming. Genet- ic And Evolutionary Computation Conference, pages 901- 908, 2010.","title":"Towards an Understanding of Locality in Genetic Programming","authors":"Edgar Galván, López, James Mcdermott, Michael O Neill, Anthony Brabazon","year":"2010","venue":"Genetic And Evolutionary Computation Conference","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Nemhauser et al., 1978] G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. An analysis of approximations for max- imizing submodular set functions-I. Mathematical Pro- gramming, 14(1):265-294, 1978.","title":"An analysis of approximations for maximizing submodular set functions-I","authors":"G L Nemhauser, L A Wolsey, M L Fisher","year":"1978","venue":"Mathematical Programming","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Nguyen et al., 2007] Q H Nguyen, Y S Ong, and N Krasno- gor. A study on the design issues of Memetic Algorithm. 2007 Ieee Congress on Evolutionary Computation, Vols 1- 10, Proceedings, pages 2390-2397, 2007.","title":"A study on the design issues of Memetic Algorithm","authors":"Q H Nguyen, Y S Ong, Krasnogor","year":"2007","venue":"Ieee Congress on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Qu et al., 2015] Guannan Qu, Dave Brown, and Na Li. Distributed Greedy Algorithm for Satellite Assignmen- t Problem with Submodular Utility Function. IFAC- PapersOnLine, 48(22):258-263, 2015.","title":"Distributed Greedy Algorithm for Satellite Assignment Problem with Submodular Utility Function","authors":"Qu","year":"2015","venue":"IFAC-PapersOnLine","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Steinbach et al., 2000] Michael Steinbach, George Karypis, and Vipin Kumar. A comparison of document clustering techniques. Proceedings of KDD-2000 workshop on text mining, pages 1-20, 2000.","title":"A comparison of document clustering techniques","authors":"Michael Steinbach, George Karypis, Vipin Kumar","year":"2000","venue":"Proceedings of KDD-2000 workshop on text mining","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Trunfio, 2015] Giuseppe Trunfio. A cooperative coevolu- tionary differential evolution algorithm with adaptive sub- components. Procedia Computer Science, 51:834-844, 2015.","title":"A cooperative coevolutionary differential evolution algorithm with adaptive subcomponents","authors":"Giuseppe Trunfio","year":"2015","venue":"Procedia Computer Science","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tsujimura and Gen, 1998] Y. Tsujimura and M. Gen. Entropy-based genetic algorithm for solving TSP. 1998 Second International Conference. Knowledge-Based In- telligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111), 2(April):21-23, 1998.","title":"Second International Conference. Knowledge-Based Intelligent Electronic Systems","authors":"Y Tsujimura, M Gen","year":"1998","venue":"Proceedings KES'98","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wright et al., 2003] Alden H. Wright, Michael D. Vose, and Jonath E. Rowe. Implicit parallelism. In Genetic and Evo- lutionary Computation, pages 1505-1517, 2003.","title":"Implicit parallelism","authors":"Alden H Wright, Michael D Vose, Jonath E Rowe","year":"2003","venue":"Genetic and Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yang et al., 2013] Shengxiang Yang, Miqing Li, Xiaohui Liu, and Jinhua Zheng. A grid-based evolutionary algo- rithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 17(5):721-736, 2013.","title":"A grid-based evolutionary algorithm for many-objective optimization","authors":"Yang","year":"2013","venue":"IEEE Transactions on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yew-Soon Ong et al., 2006] Yew-Soon Ong, Zongzhao Zhou, and Dudy Lim. Curse and Blessing of Uncer- tainty in Evolutionary Algorithm Using Approximation. 2006 IEEE International Conference on Evolutionary Computation, 639798:2928-2935, 2006.","title":"Curse and Blessing of Uncertainty in Evolutionary Algorithm Using Approximation","authors":"Yew-Soon Ong","year":"2006","venue":"IEEE International Conference on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhou et al., 2016] Lei Zhou, Liang Feng, Jinghui Zhong, Yew-Soon Ong, Zexuan Zhu, and Edwin Hsing-Mean Sha. Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem. In IEEE Symposium Series on Computational Intelligence, pages 1-8, 2016.","title":"Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem","authors":"Lei Zhou, Liang Feng, Jinghui Zhong, Yew-Soon Ong, Zexuan Zhu, Edwin Hsing-Mean Sha","year":"2016","venue":"IEEE Symposium Series on Computational Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}