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Searching for resource-efficient programs: low-power pseudorandom number generators

Published: 12 July 2008 Publication History

Abstract

Non-functional properties of software, such as power consumption and memory usage, are important factors in designing software for resource-constrained platforms. This is an area where Search-Based Software Engineering has yet to be applied, and this paper investigates the potential of using Genetic Programming and Multi-Objective Optimisation as key tools in satisfying non-functional requirements. We outline the benefits of such an approach and give an example application of evolving pseudorandom number generators and performing power-functionality trade-offs.

References

[1]
Ent: A pseudorandom number sequence test program.http://www.fourmilab.ch/random/.
[2]
Mersenne Twister PRNG, University of Hiroshima. http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html.
[3]
M. Berthold and D. J. Hand. Intelligent Data Analysis: An Introduction. Springer-Verlag, 1999.
[4]
B. Bouyssounouse and J. Sifakis, editors. Embedded Systems Design: The ARTIST Roadmap for Research and Development. Springer, 2005.
[5]
D. Brooks, V. Tiwari, and M. Martonosi. Wattch: a framework for architectural-level power analysis and optimizations. In ISCA, pages 83--94, 2000.
[6]
D. Burger, T. M. Austin, and S. Bennett. Evaluating Future Microprocessors: The Simple Scalar Tool Set.Technical Report CS-TR-1996-1308, Computer Sciences Department. University of Wisconsin-Madison, 1996.
[7]
J. Clark, J. Dolado, M. Harman, R. Hierons, B. Jones, M. Lumkin, B. Mitchell, S. Mancoridis, K. Rees, M. Roper, and M. Shepperd. Reformulating software engineering as a search problem. Software, IEE Proceedings, 150:161--175, 2003.
[8]
C. A. Coello. An updated survey of GA-based multiobjective optimization techniques. ACM Computing Surveys, 32(2):109--143, 2000.
[9]
K. Deb. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, 2001.
[10]
R. Forre. The Strict Avalanche Criterion: spectral properties of boolean functions and an extendeddefinition. In CRYPTO '88: Proceedings on Advances in cryptology, pages 450--468. Springer-Verlag, 1990
[11]
J. C. Hernandez, P. Isasi, and A. Seznec. On the design of state-of-the-art pseudo random number generators by means of genetic programming. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pages 1510--1516, 2004.
[12]
J. Jannink. Cracking and Co-Evolving Randomizers. Advances in Genetic Programming, pages 425--443, 1994.
[13]
D. E. Knuth. Art of Computer Programming, Volume 2: Seminumerical Algorithms (3rd Edition). Addison-Wesley, November 1997.
[14]
J. R. Koza. Hierarchical genetic algorithms operating on populations of computer programs. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence IJCAI-89, volume 1, pages 768--774. Morgan Kaufmann, 1989.
[15]
J. R. Koza. Evolving a computer program to generate random numbers using the genetic programming paradigm. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 37--44. Morgan Kaufmann, 1991.
[16]
C. Lamenca-Martinez, J. C. Hernandez-Castro, J. M. Estevez-Tapiador, and A. Ribagorda. Lamar: A new pseudorandom number generator evolved by means of genetic programming. In Parallel Problem Solving from Nature IX, volume 4193, pages 850--859. Springer-Verlag, 2006.
[17]
S. Luke. ECJ: A Java-based Evolutionary Computation Research System. http://cs.gmu.edu/~eclab/projects/ecj/, 2007.
[18]
B. Mesman, L. Spaanenburg, H. Brinksma, E. Deprettere, E. Verhulst, F. Timmer, H. van Gageldonk, L. Eggermont, R. van Leuken, T. Krol, and W. Hendriksen. Embedded Systems Roadmap - Vision on technology for the future of PROGRESS. STW Technology Foundation, 2002.
[19]
M. O'Neill and C. Ryan. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Springer, 2003.
[20]
A. Sinha, A. Wang, and A. P. Chandrakasan. Algorithmic transforms for efficient energy scalable computation. In ISLPED '00: Proceedings of the 2000 international symposium on Low power electronics and design, pages 31--36. ACM Press, 2000.
[21]
M. Sipper and M. Tomassini. Co-evolving parallel random number generators. In Parallel Problem Solving from Nature - PPSN IV, pages 950--959. Springer, 1996.
[22]
A. F. Webster and S. E. Tavares. On the design of S-boxes. In Advances in Cryptology - Crypto '85, pages 523--534. Springer-Verlag, 1986.
[23]
E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Swiss Federal Institute of Technology, 2001.

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cover image ACM Conferences
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
July 2008
1814 pages
ISBN:9781605581309
DOI:10.1145/1389095
  • Conference Chair:
  • Conor Ryan,
  • Editor:
  • Maarten Keijzer
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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Author Tags

  1. automatic programming
  2. genetic programming
  3. multi-objective optimisation
  4. non-functional requirements
  5. search based software engineering

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  • (2023)Exploring Genetic Improvement of the Carbon Footprint of Web PagesSearch-Based Software Engineering10.1007/978-3-031-48796-5_5(67-83)Online publication date: 4-Dec-2023
  • (2023)Developer Views on Software Carbon Footprint and Its Potential for Automated ReductionSearch-Based Software Engineering10.1007/978-3-031-48796-5_3(35-51)Online publication date: 4-Dec-2023
  • (2021)Genetic Improvement of Data for Maths FunctionsACM Transactions on Evolutionary Learning and Optimization10.1145/34610161:2(1-30)Online publication date: 29-Jul-2021
  • (2021)Exploring the Accuracy – Energy Trade-off in Machine Learning2021 IEEE/ACM International Workshop on Genetic Improvement (GI)10.1109/GI52543.2021.00011(11-18)Online publication date: May-2021
  • (2021)Uniform Edit Selection for Genetic Improvement: Empirical Analysis of Mutation Operator Efficacy2021 IEEE/ACM International Workshop on Genetic Improvement (GI)10.1109/GI52543.2021.00009(1-8)Online publication date: May-2021
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  • (2018)Specialising Software for Different Downstream Applications Using Genetic Improvement and Code TransplantationIEEE Transactions on Software Engineering10.1109/TSE.2017.270260644:6(574-594)Online publication date: 1-Jun-2018
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