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GPS: a constraint-based gene position procurement in chromosome for solving large-scale multiobjective multiple knapsack problems

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Abstract

The multiple knapsack problem (MKP) forms a base for resolving many real-life problems. This has also been considered with multiple objectives in genetic algorithms (GAs) for proving its efficiency. GAs use self-adaptability to effectively solve complex problems with constraints, but in certain cases, self-adaptability fails by converging toward an infeasible region. This pitfall can be resolved by using different existing repairing techniques; however, this cannot assure convergence toward attaining the optimal solution. To overcome this issue, gene position-based suppression (GPS) has been modeled and embedded as a new phase in a classical GA. This phase works on the genes of a newly generated individual after the recombination phase to retain the solution vector within its feasible region and to improve the solution vector to attain the optimal solution. Genes holding the highest expressibility are reserved into a subset, as the best genes identified from the current individuals by replacing the weaker genes from the subset. This subset is used by the next generated individual to improve the solution vector and to retain the best genes of the individuals. Each gene’s positional point and its genotype exposure for each region in an environment are used to fit the best unique genes. Further, suppression of expression in conflicting gene’s relies on the requirement toward the level of exposure in the environment or in eliminating the duplicate genes from the environment. TheMKP benchmark instances from the OR-library are taken for the experiment to test the new model. The outcome portrays that GPS in a classical GA is superior in most of the cases compared to the other existing repairing techniques.

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References

  1. Azad M A K, Rocha A M A C, Fernandes E M G P. Improved binary artificial fish swarm algorithm for the 0–1 multidimensional knapsack problems. Swarm and Evolutionary Computation, 2014, 14: 66–75

    Article  MATH  Google Scholar 

  2. Petersen C C. Computational experience with variants of the Balas algorithm applied to the selection of R&D projects. Management Science, 1967, 13(9): 736–750

    Article  Google Scholar 

  3. Weingartner H M. Mathematical programming and the analysis of capital budgeting problems. Englewoods Cliffs, NJ: Prentice-Hall, 1963.

    Google Scholar 

  4. Gavish B, Pirkul H. Efficient algorithms for solving multiconstraint zero-one knapsack problems to optimality. Mathematical programming, 1985, 31(1): 78–105

    Article  MathSciNet  MATH  Google Scholar 

  5. Shih W. A branch and bound method for the multiconstraint zero-one knapsack problem. Journal of the Operational Research Society, 1979: 369–378

    Google Scholar 

  6. Pisinger D. Algorithms for knapsack problems. Dissertation for the Doctoral Degree. Copenhagen: University of Copenhagen, 2000

    Google Scholar 

  7. Coello C A C, Lamont G B, Van Veldhuizen D A. Evolutionary Algorithms for Solving Multi-objective Problems. New York: Springer, 2007

    MATH  Google Scholar 

  8. He J, Mitavskiy B, Zhou Y. A theoretical assessment of solution quality in evolutionary algorithms for the knapsack problem. In: Proceedings of IEEE Congress on Evolutionary Computation. 2014: 141–148

    Google Scholar 

  9. Ibarra O H, Kim C E. Fast approximation algorithms for the knapsack and sum of subset problems. Journal of the ACM(JACM), 1975, 22(4): 463–468

    Article  MathSciNet  MATH  Google Scholar 

  10. Bansal J C, Deep K. A modified binary particle swarm optimization for knapsack problems. Applied Mathematics and Computation, 2012, 218(22): 11042–11061

    Article  MathSciNet  MATH  Google Scholar 

  11. Deb K, Pratap A, Agarwal S, Meyarivan T A M T. A fast and elitist multiobjective genetic algorithm: NSGA–II. IEEE Transaction on Evolutionary Computation, 2002, 6(2): 182–197

    Article  Google Scholar 

  12. Li Z Y, Rudolph G, Li K L. Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms. Computers and Mathematics with Applications, 2014, 57: 1843–1854

    Article  MathSciNet  MATH  Google Scholar 

  13. Kumar R, Rockett P. Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: a learning follows-decomposition strategy. IEEE Transactions on Neural Networks, 1998, 9(5): 822–830

    Article  Google Scholar 

  14. Bosman P A N, Thierens D. The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Transaction on Evolutionary Computation, 2003, 7(2): 174–188

    Article  Google Scholar 

  15. Erlebach T, Kellerer H, Pferschy U. Approximating multiobjective knapsack problems. In: Proceedings of Workshop on Algorithms and Data Structures. 2001, 210–221

    Chapter  Google Scholar 

  16. Kumar R, Banerjee N. Analysis of a multiobjective evolutionary algorithm on the 0–1 knapsack problem. Theoretical Computer Science, 2006, 358(1): 104–120

    Article  MathSciNet  MATH  Google Scholar 

  17. Paul P V, Ramalingam A, Baskaran R, Dhavachelvan P, Vivekanandan K, Subramanian R. A new population seeding technique for permutation-coded genetic algorithm: service transfer approach. Journal of Computational Science, 2014, 5(2): 277–297

    Article  Google Scholar 

  18. van Kampen A H C, Strom C S, Buydens L M C. Lethalization, penalty and repair functions for constraint handling in the genetic algorithm methodology. Chemometrics and Intelligent Laboratory Systems, 1996, 34(1): 55–68

    Article  Google Scholar 

  19. Uyar S, Eryigit G. Improvements to penalty-based evolutionary algorithms for the multi-dimensional knapsack problem using a gene-based adaptive mutation approach. In: Proceedings of the 7th ACM Annual Conference on Genetic and Evolutionary Computation. 2005, 1257–1264

    Google Scholar 

  20. Glover F. Advanced greedy algorithms and surrogate constraint methods for linear and quadratic knapsack and covering problems. European Journal of Operational Research, 2013, 230(2): 212–225

    Article  MathSciNet  MATH  Google Scholar 

  21. Gorski J, Paquete L, Pedrosa F. Greedy algorithms for a class of knapsack problems with binary weights. Computers & Operations Research, 2012, 39(3): 498–511

    Article  MathSciNet  MATH  Google Scholar 

  22. Wang L, Wang S Y, Xu Y. An effective hybrid EDA-based algorithm for solving multidimensional knapsack problem. Expert Systems with Applications, 2012, 39(5): 5593–5599

    Article  MathSciNet  Google Scholar 

  23. Martins J P, Fonseca C M, Delbem A C B. On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem. Neurocomputing, 2014, 146: 17–29

    Article  Google Scholar 

  24. Chih M. Self-adaptive check and repair operator-based particle swarm optimization for the multidimensional knapsack problem. Applied Soft Computing, 2015, 26: 378–389

    Article  Google Scholar 

  25. Kumar R, Rockett P. Improved sampling of the Pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm. Evolutionary computation, 2002, 10(3): 283–314

    Article  Google Scholar 

  26. Chih M, Lin C J, Chern M S, Ou T Y. Particle swarm optimization with time-varying acceleration coefficients for the multidimensional knapsack problem. Applied Mathematical Modelling, 2014, 38(4): 1338–1350

    Article  MathSciNet  Google Scholar 

  27. Michailidis J, Graves J A M, Murray N D. Suppression of positioneffect variegation in Drosophila melanogaster, by fatty acids and dimethylsulphoxide: implications for the mechanism of position-effect variegation. Journal of Genetics, 1989, 68(1): 1–8

    Article  Google Scholar 

  28. Mount S M, Anderson P. Expanding the definition of informational suppression. Trends in Genetics, 2000, 16(4): 157

    Article  Google Scholar 

  29. Manicassamy J, Dhavachelvan P. Gene transinfection directs towards gene functional enhancement using genetic algorithm. IERI Procedia, 2013, 4: 268–274

    Article  Google Scholar 

  30. Costantini F D, Roberts S, Evans E P, Burtenshaw M D, Lacy E. Position Effects and Gene Expression in the Transgenic Mouse, Transfer and Expression of Eukraryotic Genes. New York: Academic Press, 1984

    Google Scholar 

  31. Magtanong L, Ho C H, Barker S L, Jiao W, Baryshnikova A, Bahr S, Smith A M, Heisler L E, Choy J S, Kuzmin E, Andrusiak K, Kobylianski A, Li Z J, Costanzo M, Basrai M A, Giaever G, Nislow C, Andrews B, Boone C. Dosage suppression genetic interaction networks enhance functional wiring diagrams of the cell. Natures Biotechnology, 2011, 29: 505–511

    Article  Google Scholar 

  32. Barabasi A L, Oltvai Z N. Network biology: understanding the cell’s functional organization. Nature reviews. Genetics, 2004, 5(2): 101–113

    Article  Google Scholar 

  33. Hartman P E, Roth J R. Mechanisms of suppression. Advances in Genetics, 1973, 17: 1–105

    Google Scholar 

  34. Prelich G. Mechanisms of suppression: themes from variations. Trends Genetics, 1999, 15(7): 261–266

    Article  Google Scholar 

  35. Ma A N, Wang H, Guo R, Wang Y X, Li W, Cui J W, Wang G J, Hoffman A R, Hu J F. Targeted gene suppression by inducing de novo DNA methylation in the gene promoter. Journal of Epigenetics and Chromatin, 2014, 7(1): 20

    Article  Google Scholar 

  36. Lissemore J L, Currie P D, Turk CM, Maine EM. Intragenic dominant suppressors of GLP-1, a gene essential for cell-signaling in Caenorhabditis elegans, support a role for cdc10/SWI6/Ankyrin motifs in GLP-1 function. Genetics, 1993, 135(4): 1023–1034

    Google Scholar 

  37. Wu Y, Han M. Suppression of activated Let-60 ras protein defines a role of Caenorhabditis elegans Sur-1 MAP kinase in vulval differentiation. Genes & Development, 1994, 8(2): 147–159

    Article  Google Scholar 

  38. Sturtevant A H. The vermillion gene and gynandromorphism. Experimental Biology and Medicine, 1920, 17(4): 70–71.

    Article  Google Scholar 

  39. Lai X, Schmitz U, Gupta S K, Bhattacharya A, Kunz M, Wolkenhauer O, Vera J. Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs. Nucleic Acid Research, 2012, 40(18): 8818–8834

    Article  Google Scholar 

  40. Guo S W. Proportion of genes survived in offspring conditional on inheritance of flanking markers. Genetics, 1994, 138(3): 953–962

    Google Scholar 

  41. Yang N, Hu F, Zhou L X, Tang J J. Reconstruction of ancestral gene orders using probabilistic and gene encoding approaches. PloS One, 2014, 9(10): e108796

    Article  Google Scholar 

  42. Seo M, Oh S. Derivation of an artificial gene to improve classification accuracy upon gene selection. Computational Biology and Chemistry, 2012, 36: 1–12

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Pondicherry University, Pondicherry, 605 014, India

    Jayanthi Manicassamy, Dinesh Karunanidhi, Sujatha Pothula, Vengattaraman Thirumal, Dhavachelvan Ponnurangam & Subramanian Ramalingam

Authors
  1. Jayanthi Manicassamy
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  2. Dinesh Karunanidhi
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  3. Sujatha Pothula
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  4. Vengattaraman Thirumal
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  5. Dhavachelvan Ponnurangam
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  6. Subramanian Ramalingam
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Corresponding author

Correspondence to Jayanthi Manicassamy.

Additional information

Jayanthi Manicassamy is a research scholar, and is pursuing the PhD degree in the Department of Computer Science, Pondicherry University (PU), India. She has completed her M.C.A. degree from Madras University, India and M.Tech. degree in computer science and engineering, PU. Currently she is working in the fields of evolutionary computing.

Dinesh Karunanidhi is a research scholar, and is pursuing his PhD degree in the Department of Computer Science, Pondicherry University (PU), India. He has completed his BE degree in computer science from Sri Aravindar Engineering College, India and M.Tech. in network and Internet engineering, PU. Currently he is working in the fields of optimization algorithms.

Sujatha Pothula is an assistant professor in the Department of CSE, Pondicherry University (PU), India. Previously, she received her M.Tech. and PhD degrees in computer science and engineering from PU in 2005 and 2012, respectively. Her research interests include wireless sensor networks, information systems, and performance evaluation. She holds three books in information retrieval and cloud computing, and has published several research articles including more than thirty five proceedings and journal publications. She serves for the Technical Committee of International Arab Journal of e-Technology.

Vengattaraman Thirumal is currently an assistant professor in the Department of Computer Science, Pondicherry University (PU), India. He completed his BE degree in computer science and engineering (2004) and M.Tech. in computer science and engineering (2006). He obtained his PhD degree in computer science and engineering from PU in 2010. He has around ten years of experience in the education and research. His research areas include evolutionary computing, service computing, software engineering, multi-agent, and Web services. He has published more than 55 research articles in International & National Journals, Conferences and Books. He is the member of various National and International bodies like The Institution of Electronics and Telecommunication Engineers, Computer Society of India (CSI) and International Network for Engineering Education. Moreover, he is the principal investigator for UGC major research project, India.

Dhavachelvan Ponnurangam is a professor in the Department of Computer Science, Pondicherry University (PU), India. He completed his B.Tech. in electrical and electronics engineering from Madras University, India in 1997. He obtained his M.Tech. in computer science and engineering (2000) and PhD in computer science and engineering (2007) from Anna University, India. He has around 15 years of experience as an academician, researcher and administrator. Presently he is heading the Department of Computer Science, Pondicherry Central University, India. His research areas include software engineering, Web service computing and evolutionary algorithms. As the main and coauthor, he has more than 100 publications in his credit. The publication list includes national and international journals, conferences, books and book chapters.

Subramanian Ramalingam is the senior professor in the Department of Computer Science, Pondicherry Central University (PCU), India. He completed his BS in mathematics in the Madurai Kamaraj University, India in 1982. He received his MS and PhD in mathematics from Indian Institute of Technology, Delhi, India in 1984 and 1989 respectively. He has around 23 years of experience in teaching and researching. As a part of administration, he had been the HOD of the Department of Computer Science, and currently he is the Dean of the School of Engineering and Technology, PCU. His specialization includes parallel & distributed systems, robotics and evolutionary algorithms. He has published more than 50 national and international journal or conference publications, books and book chapters.

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GPS: a constraint-based gene position procurement in chromosome for solving large-scale multiobjective multiple knapsack problems

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Manicassamy, J., Karunanidhi, D., Pothula, S. et al. GPS: a constraint-based gene position procurement in chromosome for solving large-scale multiobjective multiple knapsack problems. Front. Comput. Sci. 12, 101–121 (2018). https://doi.org/10.1007/s11704-016-5195-1

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