Subscribe

Issue No.12 - December (2010 vol.22)

pp: 1724-1737

Thiago Quirino , University of Miami, Coral Gables

Miroslav Kubat , University of Miami, Coral Gables

Nicholas J. Bryan , Stanford University, Palo Alto

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.211

ABSTRACT

The behavior of the genetic algorithm (GA), a popular approach to search and optimization problems, is known to depend, among other factors, on the fitness function formula, the recombination operator, and the mutation operator. What has received less attention is the impact of the mating strategy that selects the chromosomes to be paired for recombination. Existing GA implementations mostly choose them probabilistically, according to their fitness function values, but we show that more sophisticated mating strategies can not only accelerate the search, but perhaps even improve the quality of the GA-generated solution. In our implementation, we took inspiration from the "opposites-attract” principle that is so common in nature. As a testbed, we chose the problem of 1-NN classifier tuning where genetic solutions have been employed before, and are thus well-understood by the research community. We propose three "instinct-based” mating strategies and experimentally investigate their behaviors.

INDEX TERMS

Genetic algorithm, mating strategies, multiobjective optimization, nearest-neighbor classifiers.

CITATION

Thiago Quirino, Miroslav Kubat, Nicholas J. Bryan, "Instinct-Based Mating in Genetic Algorithms Applied to the Tuning of 1-NN Classifiers",

*IEEE Transactions on Knowledge & Data Engineering*, vol.22, no. 12, pp. 1724-1737, December 2010, doi:10.1109/TKDE.2009.211REFERENCES

- [1] "Opposites Attract: How Genetics Influences Humans to Choose Their Mates,"
European Soc. of Human Genetics, http://www. sciencedaily.com/releases/2009/ 05090525105435.htm, May 2009.- [2] S.-Y. Ho, C.-C. Liu, and S. Liu, "Design of an Optimal Nearest Neighbor Classifier Using an Intelligent Genetic Algorithm,"
Pattern Recognition Letters vol. 23, no. 13, pp. 1495-1503, 2002.- [3] L. Kuncheva and L. Jain, "Nearest Neighbor Classifier: Simultaneous Editing and Feature Selection,"
Pattern Recognition Letters vol. 20, pp. 1149-1156, 1999.- [4] J. Cano, F. Herrera, and M. Lozano, "Using Evolutionary Algorithms as Instance Selection for Data Reduction in KDD: An Experimental Study,"
IEEE Trans. Evolutionary Computation, vol. 7, no. 6, pp. 561-575, Dec. 2003.- [5] X. Llorà and J.M.G.i Guiu, "Prototype Induction and Attribute Selection via Evolutionary Algorithms,"
Intelligent Data Analysis, vol. 7, no. 3, pp. 193-208, 2003.- [6] L. Kuncheva and J. Bezdek, "Nearest Prototype Classification: Clustering, Genetic Algorithms, or Random Search?"
IEEE Trans. Systems, Man and Cybernetics, Part C: Applications and Rev., vol. 28, no. 1, pp. 160-164, Feb. 1998.- [7] M. Raymer, W. Punch, E. Goodman, L. Kuhn, and A. Jain, "Dimensionality Reduction Using Genetic Algorithms,"
IEEE Trans. Evolutionary Computation, vol. 4, no. 2, pp. 164-171, July 2000.- [8] D.E. Goldberg,
Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., 1989.- [9] A. Rozsypal and M. Kubat, "Selecting Representative Examples and Attributes by a Genetic Algorithm,"
Intelligent Data Analysis, vol. 7, no. 4, pp. 291-304, 2003.- [10] C.B.D. Newman and C. Merz, "UCI Repository of Machine Learning Databases," http://archive.ics.uci.eduml/, 1998.
- [11] L.J. Eshelman,
The Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination, Morgan Kauffman Inc., 1991.- [12] H. Ishibuchi and T. Nakashima, "Multi-Objective Pattern and Feature Selection By a Genetic Algorithm,"
Proc. 2000 Genetic and Evolutionary Computation Conf., pp. 1069-1076, 2000.- [13] T. Cover and P.E. Hart, "Nearest Neighbor Pattern Classification,"
IEEE Trans. Information Theory, vol. 13, no. 1, pp. 21-27, Jan. 1967.- [14] E. Fix and J.J.L. Hodges, "Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties,"
Int'l Statistical Rev., vol. 57, no. 3, pp. 238-247, Dec. 1989.- [15] J.H. Freidman, J.L. Bentley, and R.A. Finkel, "An Algorithm for Finding Best Matches in Logarithmic Expected Time,"
ACM Trans. Math. Software, vol. 3, no. 3, pp. 209-226, 1977.- [16] S.A. Nene and S.K. Nayar, "A Simple Algorithm for Nearest Neighbor Search in High Dimensions,"
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 9, pp. 989-1003, Sept. 1997.- [17] R.F. Sproull, "Refinements to Nearest-Neighbor Searching in K-Dimensional Trees,"
Algorithmica, vol. 6, no. 4, pp. 579-589, 1991.- [18] E. Horowitz, S. Sahni, and S. Rajasekaran,
Computer Algorithms, Computer Science, 1997.- [19] P. Hart, "The Condensed Nearest-Neighbor Rule,"
IEEE Trans. Information Theory, vol. IT-14, no. 3, pp. 515-516, May 1968.- [20] L. Kuncheva, "Editing for the K-Nearest Neighbors Rule By a Genetic Algorithm,"
Pattern Recognition Letters, vol. 16, no. 8, pp. 809-814, 1995.- [21] D.L. Wilson, "Asymptotic Properties of Nearest Neighbor Rules Using Edited Data,"
IEEE Trans. Systems, Man and Cybernetics, vol. 2, no. 3, pp. 408-421, July 1972.- [22] F. Angiulli, "Fast Condensed Nearest Neighbor Rule,"
Proc. 22nd ACM Int'l Conf. Machine Learning, pp. 25-32, 2005.- [23] C.K.E. Cantu-Paz, S. Newsam, "Feature Selection in Scientific Applications,"
Proc. ACM SIGKDD Internations Conf. Knowledge Discover and Data Mining, pp. 788-793, 2004.- [24] M. Cheatham and M. Rizki, "Feature and Prototype Evolution for Nearest Neighbor Classification of Web Documents,"
Proc. Third Int'l Conf. Information Technology: New Generations, pp. 364-369, 2006.- [25] R. Gil-Pita and X. Yao, "Using a Genetic Algorithm for Editing K-Nearest Neighbor Classifiers,"
Proc. Conf. Intelligent Data Eng. and Automated Learning, pp. 1141-1150, 2007.- [26] "Application of Genetic Algorithms to Feature Subset Selection in a Farsi OCR,"
Proc. World Academy of Science, Eng. and Technology, M. Soryani and N. Rafat, eds., vol. 18, pp. 364-369, Dec. 2006.- [27] S.S.S. Chen and D. Whitley, "Fast and Accurate Feature Selection Using Hybrid Genetic Strategies,"
Proc. Congress on Evolutionary Computation (CEC '99), pp. 177-184, 1999.- [28] C.A.C. Coello, "A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques,"
Knowledge and Information Systems, vol. 1, no. 3, pp. 129-156, 1999.- [29] A. Konak, D. Coit, and A. Smith, "Multi-Objective Optimization Using Genetic Algorithms: A Tutorial,"
Reliability Eng. and System Safety, vol. 91, no. 9, pp. 992-107, 2006.- [30] J.-H. Chen, H.-M. Chen, and S.-Y. Ho, "Design of Nearest Neighbor Classifiers: Multi-Objective Approach,"
Int'l J. Approximate Reasoning, vol. 40, pp. 3-22, July 2005.- [31] J. Schaffer, "Multiple Objective Optimization with Vector Evaluated Genetic Algorithms,"
Proc. First Int'l Conf. Genetic Algorithms, pp. 93-100, 1985.- [32] G. Liepins, M. Hillard, J. Richardson, and M. Palmer, "Genetic Algorithm Applications to Set Covering and Traveling Salesman Problems,"
Operations Research and Artificial Intelligence: The Integration of Problem-Solving Strategies, pp. 29-57, Kluwer Academic Publishers, 1990.- [33] B. Ritzel, J. Eheart, and S. Ranjithan, "Using Genetic Algorithms to Solve a Multiple Objective Groundwater Pollution Containment Problem,"
Water Resources Research, vol. 30, no. 5, pp. 1589-1603, 1994.- [34] N. Srinivas, K. Deb, "Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,"
IEEE Trans. Evolutionary Computation, vol. 2, no. 3, pp. 221-248, Sept. 1994.- [35] P.J. Bentley,
Evolutionary Design by Computers with CDROM. Morgan Kaufmann Publishers Inc., 1999.- [36] E. Zitzler, M. Laumanns, and L. Thiele, "Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach,"
IEEE Trans. Evolutionary Computation, vol. 3, no. 4, pp. 257-271, Nov. 1999.- [37] J. Knowles and D. Corne, "Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy,"
Evolutionary Computation, vol. 8, no. 2, pp. 149-172, June 2000.- [38] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-2,"
IEEE Trans. Evolutionary Computation, vol. 6, no. 2, pp. 182-197, Aug. 2002.- [39] E. Hart and J. Timmis, "Application Areas of AIS: The Past, the Present and the Future,"
Applied Soft Computing, vol. 8, no. 1, pp. 191-201, 2008.- [40] C. Coello and N. Cortes, "Solving Multiobjective Optimization Problems Using an Artificial Immune System,"
Genetic Programming and Evolvable Machines, vol. 6, no. 2, pp. 163-190, 2005.- [41] M. Gong, L. Jiao, H. Du, and L. Bo, "Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection,"
IEEE Trans. Evolutionary Computation, vol. 16, no. 2, pp. 225-255, 2008.- [42] E. Cantu-paz, "A Survey of Parallel Genetic Algorithms,"
Calculateurs Paralleles, vol. 10, pp. 141-171, 1997.- [43] E. Cantu-paz, "Designing Efficient Master-Slave Parallel Genetic Algorithms," Techical Report TR No.: 97004, Illinois Genetic Algorithms Laboratory, 1997.
- [44] P.B. Grosso, "Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model," Doctoral Dissertation, The Univ. of Michigan, 1985.
- [45] P.G.J. Yao and N. Kharma, "Bmpga: A Bi-Objectvive Multi-Population Genetic Algorithm for Multi-Modal Function Optimization,"
Proc. IEEE Congress Evolutionary Computation, vol. 1, pp. 816-823, 2005.- [46] M. Munetomo, Y. Takai, and Y. Sato, "An Efficient Migration Scheme for Subpopulation-Based Asynchronously Parallel Genetic Algorithms,"
Proc. Fifth Int'l Conf. Genetic Algorithms, p. 649, 1993.- [47] E.G.S. Lin and W. Punch, "Coarse-Grain Parallel Genetic Algorithms: Categorization and New Approach,"
Proc. Sixth IEEE Symp. Parallel and Distributed Processing, Oct. 1994.- [48] J.P.H. Zhu and L. Jiao, "Multi-Population Genetic Algorithm for Feature Selection,"
Natural Computation Techniques Applications, pp. 480-487, 2006.- [49] I. Rukovansky, "Optimization of the Throughput of Computer Network Based on Parallel Ea,"
Proc. World Congress on Eng. and Computer Science (WCECS '09), vol. 2, pp. 1038-1043, Oct. 2009.- [50] K.S. Goh, A. Lim, and B. Rodrigues, "Sexual Selection for Genetic Algorithms,"
Artificial Intelligence Rev., vol. 19, no. 2, pp. 123-152, 2003.- [51] M.M. Raguwanshi and O.G. Kakde, "Genetic Algorithm with Species and Sexual Selection,"
Proc. IEEE Int'l Conf. Cybernetics, Intelligent Systems, Robotics, Automation, and Mechatronics (CIS-RAM '06), 2006.- [52] Y. Zhu, Z. Yang, and J. Song, "A Genetic Algorithm with Age and Sexual Features,"
Intelligent Computing, pp. 634-640, Springer, 2006.- [53] J. Sanchez-Velazco and J.A. Bullinaria, "Sexual Selection with Competitive/Co-Operative Operators for Genetic Algorithms,"
Proc. Int'l Conf. Neural Networks and Computational Intelligence, pp. 217-223, 2003.- [54] J.S. Velazco and J.A. Bullinaria, "Gendered Selection Strategies in Genetic Algorithms for Optimization,"
Proc. UK Workshop Computational Intelligence, pp. 217-223, 2003.- [55] U. Fayyad and K. Irani, "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning,"
Proc. 13th Int'l Joint Conf. Artificial Intelligence, pp. 1022-1027, 1993. |