Design of Real-Time Computer-Based Systems Using Developmental Genetic Programming

  • Stanisław DeniziakEmail author
  • Leszek Ciopiński
  • Grzegorz Pawiński


This chapter presents applications of the developmental genetic programming (DGP) to design and optimize real-time computer-based systems. We show that the DGP approach may be efficiently used to solve the following problems: scheduling of real-time tasks in multiprocessor systems, hardware/software codesign of distributed embedded systems, budget-aware real-time cloud computing. The goal of optimization is to minimize the cost of the system, while all real-time constraints will be satisfied. Since the finding of the best solution is very complex, only efficient heuristics may be applied for real-life systems. Unlike the other genetic approaches where chromosomes represent solutions, in the DGP chromosomes represent system construction procedures. Thus, not the system architecture, but the synthesis process evolves. Finally, a tree describing the construction of a (sub-)optimal solution is obtained and the genotype-to-phenotype mapping is applied to create the target system. Some other ideas concerning other applications of the DGP for optimization of computer-based systems also are outlined.


Cloud Computing Embed System Task Assignment Task Schedule Task Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Alcaraz J, Maroto C (2001) A robust genetic algorithm for resource allocation in project scheduling. Annals of Operations Research, 102, pp. 83-109.zbMATHMathSciNetCrossRefGoogle Scholar
  2. Bąk S, Czarnecki R, Deniziak S (2013) Synthesis of real-time applications for internet of things. In: Pervasive Computing and the Networked World. Lecture Notes in Computer Science, Springer Berlin Heidelberg, p. 35-49.Google Scholar
  3. Blazewicz J, Lenstra JK, Rinnooy Kan (1983) Scheduling subject to resource constraints: Classification and complexity, Discrete Applied Mathematics, No. 5, pp. 11–24.Google Scholar
  4. Bouleimen K, Lecocq H (1998). A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem, Technical Report, Service de Robotique et Automatisation, Universite de Liege.Google Scholar
  5. Brucker P, Knust S, Schoo A, Thiele O (1998) A branch-and-bound algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research, 107: 272–288.zbMATHCrossRefGoogle Scholar
  6. Buyya R, Broberg J, Goscinski A (2011) Cloud Computing: Principles and Paradigms. Wiley Press, New York, USACrossRefGoogle Scholar
  7. Deiranlou M, Jolai F (2009) A New Efficient Genetic Algorithm for Project Scheduling under Resource Constrains. World Applied Sciences Journal, 7 (8): pp. 987-997.Google Scholar
  8. Demeulemeester EL, Herroelen WS (1997) New benchmark results for the resource-constrained project scheduling problem. Management Science, 43: 1485–1492zbMATHCrossRefGoogle Scholar
  9. Demeulemeester EL, Herroelen WS (2002) Project Scheduling. A Research Handbook, SpringerzbMATHGoogle Scholar
  10. Deniziak S (2004) Cost-efficient synthesis of multiprocessor heterogeneous systems. Control and Cybernetics 33: 341–355Google Scholar
  11. Deniziak S, Górski A (2008) Hardware/Software Co-Synthesis of Distributed Embedded Systems Using Genetic Programming. Lecture Notes in Computer Science, Springer-Verlag, pp. 83-93.Google Scholar
  12. Deniziak S, Wieczorek S (2012a) Parallel Approach to the Functional Decomposition of Logical Functions Using Developmental Genetic Programming. Lecture Notes in Computer Science 7203:406-415.CrossRefGoogle Scholar
  13. Deniziak S, Wieczorek S (2012b) Evolutionary Optimization of Decomposition Strategies for Logical Functions. Lecture Notes in Computer Science 7269, pp. 182-189CrossRefGoogle Scholar
  14. Deniziak S, Ciopiński L, Pawiński G et al (2014) Cost Optimization of Real-Time Cloud Applications Using Developmental Genetic Programming, IEEE/ACM 7th International Conference on Utility and Cloud ComputingGoogle Scholar
  15. Dick RP, Jha NK (1998) MOGAC: A Multiobjective Genetic Algorithm for the Co-Synthesis of Hardware-Software Embedded Systems. IEEE Trans. on Computer Aided Design of Integrated Circuits and Systems 17(10):920–935CrossRefGoogle Scholar
  16. Drexl A, Kimms A (2001) Optimization guided lower and upper bounds for the resource investment problem, Journal of the Operational Research Society 52 pp. 340–351zbMATHCrossRefGoogle Scholar
  17. Dorndorf U, Pesch E and Toàn Phan-Huy (2000) Constraint propagation techniques for the disjunctive scheduling problem. Artificial intelligence 122.1 (2000): 189-240.Google Scholar
  18. Dorigo M, Stützle T (2004) Ant Colony Optimization. Massachusetts Institute of Technology, USAGoogle Scholar
  19. Frankola T, Golub M and Jakobovic D (2008) Evolutionary algorithms for the resource constrained scheduling problem. In Proceedings of 30th International Conference on Information Technology Interfaces 7269:715-722Google Scholar
  20. Hartmann S (1998) A Competitive Genetic Algorithm for Resource-Constrained Project Scheduling. Naval Research Logistics, 45:733-750zbMATHMathSciNetCrossRefGoogle Scholar
  21. Hartmann S, Briskorn D (2010) A survey of variants and extensions of the resource-constrained project scheduling problem. European journal of operational research : EJOR. - Amsterdam : Elsevier 207, 1 (16.11.), pp. 1-15Google Scholar
  22. Hendrickson C, Tung A (2008) Advanced Scheduling Techniques. In: Project Management for Construction, (2.2 ed.), Prentice HallGoogle Scholar
  23. Keller R, Banzhaf W (1999) The Evolution of Genetic Code in Genetic Programming. In: Proc. of the Genetic and Evolutionary Computation Conference, pp. 1077–1082Google Scholar
  24. Klein R, (2000) Scheduling of Resource-Constrained Projects. Springer Science & Business MediazbMATHCrossRefGoogle Scholar
  25. Kolish R, Sprecher A (1996) Psplib - a project scheduling library. European journal of operational research, 96:205-216.CrossRefGoogle Scholar
  26. Kolisch R, Hartmann S (1999) Heuristic algorithms for the resource-constrained project scheduling problem: Classification and computational analysis. Springer USGoogle Scholar
  27. Kolisch R, Hartmann S (2006) Experimental investigation of heuristics for resource-constrained project scheduling: An update. European journal of operational research, 174:23-37zbMATHCrossRefGoogle Scholar
  28. Koza JR (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, USAzbMATHGoogle Scholar
  29. Koza J, Keane MA, Streeter MJ et al. (2003) Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publisher, NorwellGoogle Scholar
  30. Koza JR (2010) Human-competitive results produced by genetic programming. In Genetic Programming and Evolvable Machines, pp. 251-284Google Scholar
  31. Nubel H (2001) The resource renting problem subject to temporal constraints. OR Spektrum 23: 359–381MathSciNetCrossRefGoogle Scholar
  32. Pawiński G. Sapiecha K (2012) Resource allocation optimization in Critical Chain Method. Annales Universitatis Mariae Curie-Sklodowska sectio Informaticales, 12 (1), p 17–29Google Scholar
  33. Pawiński G, Sapiecha K (2014a) Cost-efficient project management based on critical chain method with partial availability of resources. CONTROL AND CYBERNETICS, 43(1)Google Scholar
  34. Pawiński G, Sapiecha K (2014b) A Developmental Genetic Approach to the cost/time trade-off in Resource Constrained Project Scheduling. IEEE Federated Conference on Computer Science and Information SystemsGoogle Scholar
  35. Pinedo M, Chao X (1999) Operations Scheduling with applications in Manufacturing. Irwin/McGraw-Hill, Boston, New York, NY, USA, 2nd edition.Google Scholar
  36. Sapiecha K, Ciopiński L, Deniziak S (2014) An Application of Developmental Genetic Programming for Automatic Creation of Supervisors of Multitask Real-Time Object-Oriented Systems. IEEE Federated Conference on Computer Science and Information Systems, 2014.Google Scholar
  37. Tomassini M (1999) Parallel and distributed evolutionary algorithms: A review. In P. Neittaanmki K. Miettinen, M. Mkel and J. Periaux, editors, Evolutionary Algorithms in Engineering and Computer Science, J. Wiley and Sons, ChichesterGoogle Scholar
  38. Watson JD, Hopkins NH, Roberts JW et al. (1992). Molecular Biology of the Gene. Benjamin Cummings. Menlo Park, CA.Google Scholar
  39. Węglarz J et al. (2011) Project scheduling with finite or infinite number of activity processing modes–A survey. European Journal of Operational Research 208.3: 177-205.zbMATHMathSciNetCrossRefGoogle Scholar
  40. Yen, TY, Wolf WH (1995) Sensitivity-Driven Co-Synthesis of Distributed Embedded Systems. In: Proc. of the Int. Symposium on System Synthesis, pp. 4–9Google Scholar
  41. Yen, TY, Wolf WH (1997) Yen, T.-Y., Wolf, W.: Hardware-Software Co-synthesis of Distributed Embedded Systems. Springer, HeidelbergGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stanisław Deniziak
    • 1
    Email author
  • Leszek Ciopiński
    • 1
  • Grzegorz Pawiński
    • 1
  1. 1.Kielce University of TechnologyKielcePoland

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