Abstract
In this paper, we introduce polygene-based evolution, a novel framework for evolutionary algorithms (EAs) that features distinctive operations in the evolutionary process. In traditional EAs, the primitive evolution unit is a gene, wherein genes are independent components during evolution. In polygene-based evolutionary algorithms (PGEAs), the evolution unit is a polygene, i.e., a set of co-regulated genes. Discovering and maintaining quality polygenes can play an effective role in evolving quality individuals. Polygenes generalize genes, and PGEAs generalize EAs. Implementing the PGEA framework involves three phases: (I) polygene discovery, (II) polygene planting, and (III) polygene-compatible evolution. For Phase I, we adopt an associative classification-based approach to discover quality polygenes. For Phase II, we perform probabilistic planting to maintain the diversity of individuals. For Phase III, we incorporate polygene-compatible crossover and mutation in producing the next generation of individuals. Extensive experiments on function optimization benchmarks in comparison with the conventional and state-of-the-art EAs demonstrate the potential of the approach in terms of accuracy and efficiency improvement.
Similar content being viewed by others
References
Wang S Q, Gao B J, Wang S L, Cao G B, and Yin Y L. Polygene-based evolution: a novel framework for evolutionary algorithms. In: Proceedings of the 21st ACM Conference on Information and Knowledge Management. 2012, 2263–2266
Boughanem M, Tamine L. Query optimization using an improved genetic algorithm. In: Proceedings of the 9th International Conference on Information and Knowledge Management. 2000, 368–373
Venkatraman S, Yen G G. A generic framework for constrained optimization using genetic algorithms. IEEE Transactions on Evolutionary Computation, 2005, 9(4): 424–435
Malek H, Ebadzadeh M M, Rahmati M. Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm. Applied Intelligence, 2012, 37(2): 280–289
Zafra A, Ventura S. Multi-objective genetic programming for multiple instance learning. In: Proceedings of the 18th European Conference on Machine Learning. 2007, 790–797
Chang D X, Zhang X D, Zheng CW. A genetic algorithm with gene rearrangement for k-means clustering. Pattern Recognition, 2009, 42(7): 1210–1222
Özyer T, Alhajj R. Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer. Applied Intelligence, 2009, 31(3): 318–331
Wang S Q, Ma J, and Liu J M. Learning to rank using evolutionary computation: Immune programming or genetic programming? In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. 2009, 1879–1882
Kaya M Alhajj R. Utilizing genetic algorithms to optimize membership functions for fuzzy weighted association rules mining. Applied Intelligence, 2006, 24(1): 7–15
Weale T, Seitzer J. EVOC: a music generating system using genetic algorithms. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence. 2003, 1383–1384
Bryden K M, Ashlock D A, Corns S M, Willson S J. Graph-based evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2006, 10(5): 550–567
Ishibuchi H, Tsukamoto N, Nojima Y. Diversity improvement by nongeometric binary crossover in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, 2010, 14(6): 985–998
Qu B Y, Suganthan P N, and Liang J J. Differential evolution with neighborhood mutation for multimodal optimization. IEEE Transactions on Evolutionary Computation, 2012, 16(5): 601–614
Mabu S, Hirasawa K, Hu J L. A graph-based evolutionary algorithm: genetic network programming (GNP) and its extension using reinforcement learning. Evolutionary Computation, 2007, 15(3): 369–398
Hu T, Chen Y Z P, Banzhaf W. WiMAX network planning using adaptive-population-size genetic algorithm. In: Proceedings of the International Conference on Applications of Evolutionary Computation. 2010, 31–40
Zhang J, Chung H S H, Lo W L. Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Transactions on Evolutionary Computation, 2007, 11(3): 326–335
Cross A D J, Myers R, Hancock E R. Convergence of a hill-climbing genetic algorithm for graph matching. Pattern Recognition, 2000, 33(11): 1863–1880
Tantar A A, Melab N, Talbi E G. A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Computing, 2008, 12(12): 1185–1198
Chen Y P, Goldberg D E. Introducing start expression genes to the linkage learning genetic algorithm. In Proceedings of the 7th International Conference on Parallel Problem Solving from Nature. 2002, 351–360
Chen Y P, Peng W C, Jian M C. Particle swarm optimization with recombination and dynamic linkage discovery. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2007, 37(6): 1460–1470
Goldman B W, Tauritz D R. Linkage tree genetic algorithms: Variants and analysis. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation Conference. 2012, 625–632
Shao K Y, Li F, Jiang B Y, Wang N, Zhang H Y, Li W C. Neural network optimization based on improved diploidic genetic algorithm. In: Proceedings of the International Conference on Machine Learning and Cybernetics. 2010, 1470–1475
Manjari K M, Gallagher M. Variable screening for reduced dependency modelling in gaussian-based continuous estimation of distribution algorithms. In: Proceedings of IEEE Congress on Evolutionary Computation. 2012, 1–8
Rastegar R. On the optimal convergence probability of univariate estimation of distribution algorithms. Evolutionary Computation, 2011, 19(2): 225–248
Lawrence E. Henderson’s Dictionary of Biology. New York: Pearson/Prentice Hal, 2005
Lewis R. Human Genetics: Concepts and Applications. New York: McGraw Hill, 2002
Mather K M, Jinks J L. Biometrical Genetics. 3rd ed. London: Chapman and Hall, 1982
Beurton P J, Falk R, and Rheinberger H J. The Concept of the Gene in Development and Evolution. Cambridge: Cambridge University Press, 2000
Gilbert S F. Developmental Biology. 6th ed. Sunderland, MA: Sinauer Associates, 2000
Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 1993, 207–216
Liu B, Hsu W, Ma Y M. Integrating classification and association rule mining. In: Proceedings of the 4th ACM SIGKDD International Conference on Knowledge Discovery in Databases. 1998, 443–447
Holland J H. Adaptation in Natural and Artificial Systems. Cambridge, MA: The MIT Press, 1975
Han J W, Pei J, Yin Y W. Mining frequent patterns without candidate generation. In: Proceeding of ACM SIGMOD International Conference on Management of Data. 2000, 1–12
Agarwal R, Aggarwal C C, Prasad V V V. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing, 2001, 61(3): 350–371
Hämäläinen W. Statapriori: an efficient algorithm for searching statistically significant association rules. Knowledge and Information Systems, 2010, 23(3): 373–399
Beil F, Ester M, Xu X W. Frequent term-based text clustering. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery in Databases. 2002, 436–442
Leung CW, Chan S C, Chung F L. A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowledge and Information Systems, 2006, 10(3): 357–381
Yan X F, Yu P S, Han J W. Graph indexing: a frequent structure-based approach. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2004, 335–346
Teredesai A, Ahmad M A, Kanodia J, Gaborski R S. Comma: a framework for integrated multimedia mining using multi-relational associations. Knowledge and Information Systems, 2006, 10(2): 135–162
Punin J, Krishnamoorthy M, Zaki M. Web Usage Mining: Languages and Algorithms. Berlin: Springer-Verlag, 2001
Liu C, Fei L, Yan X F, Han J W, Midkiff S P. Statistical debugging: a hypothesis testing-based approach. IEEE Transactions on Software Engineering, 2006, 32(10): 831–848
Han J W, Cheng H, Xin D, Yan X F. Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery, 2007, 15(1): 55–86
Dong G Z, Li J Y. Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery in Databases. 1999, 43–52
Li J Y, Dong G Z, Ramamohanarao K. Making use of the most expressive jumping emerging patterns for classification. In: Proceeding of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2000, 131–145
Li W M, Han J W, Pei J. CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceeding of the International Conference on Data Mining. 2001, 369–376
Yin X X, Han J W. CPAR: classification based on predictive association rules. In: Proceeding of SIAM International Conference on Data Mining. 2003, 331–335
Cong G. Mining top-k covering rule groups for gene expression data. In Proceedings of the 24th ACM SIGMOD International Conference on Management of Data. 2005, 670–681
Ting C K, ZengWM, Lin T C. Linkage discovery through data mining. IEEE Computational Intelligence Magazine, 2010, 5(1): 10–13
Chen Y P, Goldberg D E. Convergence time for the linkage learning genetic algorithm. Evolutionary Computation, 2005, 13(3): 279–302
Ng K P, Wong K C. A new diploid scheme and dominance change mechanism for non-stationary function optimization. In: Proceedings of the 6th International Conference on Genetic Algorithms. 1995, 159–166
Baluja S. Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163. 1994
Tang K, Yao X, Suganthan P N, MacNish C, Chen Y P, Chen C M, Yang Z. Benchmark functions for the CEC’ 2008 special session and competition on large scale global optimization. Technical Report. 2007
Acknowledgments
The authors would like to thank Prof. Xin Yao for discussions and advice on this manuscript. This research was supported in part by the NSFC Joint Fund with Guangdong of China under Key Project (U1201258), the National Natural Science Foundation of China (Grant Nos. 71402083, 61573219, 61502258) and the National Science Foundation of Shandong Province (ZR2014FQ007).
Author information
Authors and Affiliations
Corresponding author
Additional information
A preliminary version of this paper was published in the Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM) [1]
Shuaiqiang Wang received the PhD and BS degrees in computer science from Shandong University, China in 2009 and 2004 respectively. Currently he is an assistant professor at University of Jyvaskyla, Finland. Before that, He was an associate professor at Shandong University of Finance and Economics, China from 2011 to 2014, and a postdoctoral research associate at Texas State University, USA in 2010. His research interests include information retrieval and data mining.
Yilong Yin is a professor of computer science and the director of the MLA Lab in Shandong University, China. He received his PhD degree from Jilin University, China in 2000. From 2000 to 2002, he worked as a post-doctoral fellow in the Department of Electronic Science and Engineering, Nanjing University, China. His research interests include machine learning, data mining, computational medicine and biometrics.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Wang, S., Yin, Y. Polygene-based evolutionary algorithms with frequent pattern mining. Front. Comput. Sci. 12, 950–965 (2018). https://doi.org/10.1007/s11704-016-6104-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-016-6104-3