Skip to main content

Advertisement

SpringerLink
Log in

An Intelligent Programmed Genetic Algorithm with advanced deterministic diversity creating operator using objective surface visualization

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

This paper presents a new fast Intelligent Programmed Genetic Algorithm (IPGA) based evolutionary optimization algorithm which requires lesser number of objective function evaluation for reaching optima. The proposed algorithm, apart from using probabilistic genetic operator, i.e. crossover and mutation, also uses a deterministic diversity creating operator for generating new solution in the current population. This is done by first projecting objective surface from higher dimension to lower dimension for visualization purpose and then deterministically generates new solution using some predefined rules in the region with higher objective function value. As the newly generated solution is in lower-dimensional space, these solutions are again projected back to higher dimensional space and then the objective function is evaluated at that point. The proposed IPGA is tested on three different categories of standard test functions viz. Unimodal function (2 Test Function), Unrotated Multimodal function (6 Test Function) and Rotated Multimodal function (5 Test Function). Simulation results were compared with that obtained using Binary Coded GA, Real Coded GA, recently proposed GA with Differential Evolution crossover operator (GA–DEx) and another success-history-based adaptive GA with aging mechanism (GA–aDExSPS) in terms of mean and standard deviation of the objective function, average number of objective function evaluation required to reach optima and algorithmic complexity. Simulation results clearly demonstrate better performance of the proposed IPGA when compared with other variants of GAs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Chong EKP, Zak SH (2013) An introduction to optimization, vol 76. Wiley, Hoboken

    MATH  Google Scholar 

  2. Bertsekas DP, Scientific A (2015) Convex optimization algorithms. Athena Scientific Belmont, Belmont

    Google Scholar 

  3. Zelinka I, Snasael V, Abraham A (2012) Handbook of optimization: from classical to modern approach, vol 38. Springer, Berlin

    Google Scholar 

  4. Bozorg-Haddad O (2018) Advanced optimization by nature-inspired algorithms. Springer, Berlin

    Google Scholar 

  5. Roeva O (2012) Real-world applications of genetic algorithms. BoD–Books on Demand, Norderstedt

    MATH  Google Scholar 

  6. Chibante R (2010) Simulated annealing: theory with applications. BoD–Books on Demand, Norderstedt

    Google Scholar 

  7. Pluhacek M, Senkerik R, Viktorin A, Kadavy T, Zelinka I (2017) A review of real-world applications of particle swarm optimization algorithm. In: International conference on advanced engineering theory and applications, pp 115–122

  8. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73

    Google Scholar 

  9. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Inc, Reading

    MATH  Google Scholar 

  10. Deb K, Anand A, Joshi D (2002) A computationally efficient evolutionary algorithm for real-parameter optimization. Evol Comput 10(4):371–395

    Google Scholar 

  11. Ono I, Kita H, Kobayashi S (2003) A real-coded genetic algorithm using the unimodal normal distribution crossover. In: Ghosh A, Tsutsui S (eds) Advances in evolutionary computing, Natural computing series. Springer, Berlin, Heidelberg

    Google Scholar 

  12. Eiben AE, Smith JE et al (2003) Introduction to evolutionary computing, vol 53. Springer, Berlin

    MATH  Google Scholar 

  13. Tsutsui S, Yamamura M, Higuchi T (1999) Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Proceedings of the 1st annual conference on genetic and evolutionary computation, vol 1, pp 657–664

  14. Elfeky EZ, Sarker RA, Essam DL (2008) Analyzing the simple ranking and selection process for constrained evolutionary optimization. J Comput Sci Technol 23(1):19–34

    Google Scholar 

  15. Wang Y et al (2012) A new hybrid genetic algorithm for job shop scheduling problem. Comput Oper Res 39(10):2291–2299

    MathSciNet  MATH  Google Scholar 

  16. Kurdi M (2015) A new hybrid island model genetic algorithm for job shop scheduling problem. Comput Ind Eng 88:273–283

    Google Scholar 

  17. Arumugam MS, Rao MVC, Palaniappan R (2005) New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems. Appl Soft Comput 6(1):38–52

    Google Scholar 

  18. Jayalakshmi GA, Sathiamoorthy S, Rajaram R (2001) A hybrid genetic algorithm—a new approach to solve traveling salesman problem. Int J Comput Eng Sci 2(02):339–355

    Google Scholar 

  19. Wang Y, Yao M (2009) A new hybrid genetic algorithm based on chaos and PSO. In: 2009 IEEE international conference on intelligent computing and intelligent systems, vol 1, pp 699–703

  20. Gao W (2012) Study on new improved hybrid genetic algorithm. In: Zeng D (ed) Advances in information technology and industry applications. Lecture notes in electrical engineering, vol 136. Springer, Berlin, Heidelberg

    Google Scholar 

  21. Zhang A, Sun G, Wang Z, Yao Y (2015) A hybrid genetic algorithm and gravitational search algorithm for global optimization. Neural Netw World 25(1):53

    Google Scholar 

  22. Kivijärvi J, Fränti P, Nevalainen O (2003) Self-adaptive genetic algorithm for clustering. J Heuristics 9(2):113–129

    MATH  Google Scholar 

  23. Deb K, Beyer H-G (2001) Self-adaptive genetic algorithms with simulated binary crossover. Evol Comput 9(2):197–221

    Google Scholar 

  24. Pellerin E, Pigeon L, Delisle S (2004) A meta-learning system based on genetic algorithms. Data Min Knowl Discov Theory Tools Technol VI 5433:65–73

    Google Scholar 

  25. Subbaraj P, Rengaraj R, Salivahanan S (2011) Enhancement of self-adaptive real-coded genetic algorithm using Taguchi method for economic dispatch problem. Appl Soft Comput 11(1):83–92

    Google Scholar 

  26. Lu H, Wen X, Lan L, An Y, Li X (2015) A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops. J Magn Magn Mater 374:502–507

    Google Scholar 

  27. Wei X-K, Shao W, Zhang C, Li J-L, Wang B-Z (2014) Improved self-adaptive genetic algorithm with quantum scheme for electromagnetic optimisation. IET Microw Antennas Propag 8(12):965–972

    Google Scholar 

  28. Jiang Z-Y, Cai Z-X, Wang Y (2010) Hybrid self-adaptive orthogonal genetic algorithm for solving global optimization problems. J Softw 21(6):1296–1307

    MATH  Google Scholar 

  29. Elsayed SM, Sarker RA, Essam DL (2014) A new genetic algorithm for solving optimization problems. Eng Appl Artif Intell 27:57–69

    Google Scholar 

  30. Ali MZ, Awad NH, Suganthan PN, Shatnawi AM, Reynolds RG (2018) An improved class of real-coded Genetic Algorithms for numerical optimization. Neurocomputing 275:155–166

    Google Scholar 

  31. El-Shorbagy MA, Farag MA, Mousa AA, El-Desoky IM (2019) A hybridization of sine cosine algorithm with steady state genetic algorithm for engineering design problems. In: International conference on advanced machine learning technologies and applications, pp 143–155

  32. Santos J, Ferreira A, Flintsch G (2019) An adaptive hybrid genetic algorithm for pavement management. Int J Pavement Eng 20(3):266–286

    Google Scholar 

  33. Foroughi A, Gökçen H (2019) A multiple rule-based genetic algorithm for cost-oriented stochastic assembly line balancing problem. Assem Autom 39(1):124–139

    Google Scholar 

  34. Malarvizhi N, Selvarani P, Raj P (2019) Adaptive fuzzy genetic algorithm for multi biometric authentication. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7436-4

    Article  Google Scholar 

  35. Jankauskas K, Papageorgiou LG, Farid SS (2019) Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture. Comput Chem Eng 121:212–223

    Google Scholar 

  36. Fox GC, Mansour (1991) A hybrid genetic algorithm for task allocation in multicomputers. In: Proceedings of the international conference on genetic algorithms

  37. Vlašić I, Ðurasević M, Jakobović D (2019) Improving genetic algorithm performance by population initialisation with dispatching rules. Comput Ind Eng 137:106030

    Google Scholar 

  38. Taher AA, Kadhimb SM (2019) Hybrid between genetic algorithm and artificial bee colony for key generation purpose. J Al-Qadisiyah Comput Sci Math 11(4):37

    Google Scholar 

  39. Kaliakin V (2018) Introduction to approximate solution techniques, numerical modeling, and finite element methods. CRC Press

  40. Krishnakumar K (1990) Micro-genetic algorithms for stationary and non-stationary function optimization. Intell Control Adapt Syst 1196:289–297

    Google Scholar 

  41. Boukhelifa N, Bezerianos A, Cancino W, Lutton E (2017) Evolutionary visual exploration: evaluation of an IEC framework for guided visual search. Evol Comput 25(1):55–86

    Google Scholar 

  42. Hayashida N, Takagi H (2002) Acceleration of EC convergence with landscape visualization and human intervention. Appl Soft Comput 1(4):245–256

    Google Scholar 

  43. Takagi H (2000) Active user intervention in an EC search. In: Proceedings of the fifth joint conference on information sciences, JCIS 2000, pp 995–998

  44. Bush BJ, Sayama H (2011) Hyperinteractive evolutionary computation. IEEE Trans Evol Comput 15(3):424–433

    Google Scholar 

  45. Boukhelifa N, Cancino W, Bezerianos A, Lutton E (2013) Evolutionary visual exploration: evaluation with expert users. Comput Graph Forum 32(3pt1):31–40

    Google Scholar 

  46. Takagi H (2001) Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proc IEEE 89(9):1275–1296

    Google Scholar 

  47. Koga S, Inoue T, Fukumoto M (2013) A proposal for intervention by user in interactive genetic algorithm for creation of music melody. In: 2013 International Conference on Biometrics and Kansei Engineering, pp 129–132

  48. Sun X, Gong D, Zhang W (2012) Interactive genetic algorithms with large population and semi-supervised learning. Appl Soft Comput 12(9):3004–3013

    Google Scholar 

  49. Hayashida N, Takagi H (2000) Visualized IEC: Interactive evolutionary computation with multidimensional data visualization. In: Industrial electronics society, 2000. IECON 2000. 26th annual conference of the IEEE, vol 4, pp. 2738–2743

  50. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Application of dimensionality reduction in recommender system-a case study (No. TR-00-043). Minnesota Univ Minneapolis Dept of Computer Science. Available: https://apps.dtic.mil/dtic/tr/fulltext/u2/a439541.pdf

  51. Lee JA, Verleysen M (2007) Nonlinear dimensionality reduction. Springer, Berlin

    MATH  Google Scholar 

  52. Gorban AN, Kégl B, Wunsch DC, Zinovyev AY et al (2008) Principal manifolds for data visualization and dimension reduction, vol 58. Springer, Berlin

    MATH  Google Scholar 

  53. Chatfield C (2018) Introduction to multivariate analysis. Routledge, Abingdon

    MATH  Google Scholar 

  54. Park S-H, Youn S-K (2001) The least-squares meshfree method. Int J Numer Methods Eng 52(9):997–1012

    MathSciNet  MATH  Google Scholar 

  55. Persson P-O, Strang G (2004) A simple mesh generator in MATLAB. SIAM Rev 46(2):329–345

    MathSciNet  MATH  Google Scholar 

  56. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Google Scholar 

  57. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Google Scholar 

  58. Knight A (2019) Basics of MATLAB and beyond. Chapman and Hall/CRC, Boca Raton

    MATH  Google Scholar 

  59. der Maaten LJP (2007) An introduction to dimensionality reduction using matlab. Report 1201(07–07):62

    Google Scholar 

  60. Joyce T, Herrmann JM (2018) A review of no free lunch theorems, and their implications for metaheuristic optimisation. In: Yang XS (ed) Nature-inspired algorithms and applied optimization. Studies in computational intelligence, vol 744. Springer, Cham

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, NERIST, Nirjuli, Arunachal Pradesh, 791109, India

    Devnath Shah

  2. Department of Electrical and Electronics Engineering, NIT Mizoram Aizawl, Aizawl, Mizoram, India

    Saibal Chatterjee

Authors
  1. Devnath Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saibal Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Devnath Shah.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shah, D., Chatterjee, S. An Intelligent Programmed Genetic Algorithm with advanced deterministic diversity creating operator using objective surface visualization. Evol. Intel. 13, 705–723 (2020). https://doi.org/10.1007/s12065-020-00385-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-020-00385-w

Keywords

Search

Navigation