A parallel evolutionary algorithm to optimize dynamic data types in embedded systems

Soft Computing

Volumn 12, Issue 12, 2008, Pages 1157-1167

  Risco Martín, José L ^a   Atienza, David ^a   Hidalgo, J Ignacio ^a   Lanchares, Juan ^a  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION ALGORITHMS; COMPUTER PROGRAMMING LANGUAGES; DATA STORAGE EQUIPMENT; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; HEURISTIC ALGORITHMS; HEURISTIC PROGRAMMING; INTEGRATED CIRCUITS; MULTIOBJECTIVE OPTIMIZATION; NUCLEAR PROPULSION; OPTIMIZATION; PARALLEL ALGORITHMS; SEQUENTIAL SWITCHING;

BIO-INSPIRED; DATA TYPING; DYNAMIC DATA TYPES; EMBEDDED APPLICATIONS; EMBEDDED DESIGNS; MEMORY ACCESSES; MEMORY USAGE; MULTI-OBJECTIVE GENETIC ALGORITHM; NP-COMPLETE; NSGA-II; PARALLEL EVOLUTIONARY ALGORITHMS; PARALLEL PROCESSING; POWER CONSUMPTION; SEARCHING SPACES; SEQUENTIAL ALGORITHMS; TIME-CONSUMING PROCESSES;

EMBEDDED SYSTEMS;

EID: 49049092337     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-008-0295-y     Document Type: Article

References (30)

1. Antonakos JL, Mansfield KC (1999) Practical data structures using C/C++. Prentice Hall, Englewood Cliffs
2. Atienza D, Baloukas C, Papadopoulos L, Poucet C, Mamagkakis S, Hidalgo JI, Catthoor F, Soudris D, Lanchares J (2007) Optimization of dynamic data structures in multimedia embedded systems using evolutionary computation. In SCOPES '07: Proceedings of the 10th international workshop on Software & compilers for embedded systems. ACM, New York, pp 31-40
3. Benini L, de Micheli G (2000) System level power optimization techniques and tools. ACM Trans Des Auto Electr Syst 5(2): 115-192
4. Cantú-Paz E (2000) Efficient and accurate parallel genetic algorithms. Kluwer, Dordrecht
5. Catthoor F et al (2002) Data access and storage management for embedded programmable processors. Kluwer, Dordrecht
6. Coello C (1999) A Comparative Survey of Evolutionary-based Multiobjective Optimization Techniques. Knowl Inf Syst 1: 269-308
7. Corne DW, Jerram NR, Knowles JD, Oates MJ (2001) PESA-II: Region-based selection in evolutionary multiobjective optimization. In: Proceedings of the genetic and evolutionary computation conference (GECCO-2001). Morgan Kaufmann Publishers, San Francisco, pp 283-290
8. Daylight EG, Atienza D, Vandecappelle A, Catthoor F, Mendias JM (2004) Memory-access-aware data structure transformations for embedded software with dynamic data accesses. IEEE Trans Very Large Scale Integr (VLSI) Syst 12(3): 269-280
9. Deb K (2001) Multiobjective optimization using evolutionary algorithms. Wiley, New York
10. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2): 182-197
11. de Toro Negro F, Ortega J, Ros E, Mota S, Paechter B, Martín J (2004) PSFGA: Parallel Processing and Evolutionary Computation for Multiobjective Optimisation. Parallel Comput 30: 721-739
12. Edler J, Hill MD (2007) Dinero IV trace-driven uniprocessor cache simulator. http://pages.cs.wisc.edu/~markhill/DineroIV
13. Fernandez JM, Vila P, Calle E, Marzo JL (2007) Design of Virtual topologies using the elitist team of multiobjective evolutionary algorithms. In: Proceedings of international symposium on performance evaluation of computer and telecommunication systems (SPECTS'07), pp 266-271
14. Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms, San Mateo, University of Illinois at Urbana-Champaign. Morgan Kauffman Publishers, San Francisco, pp 416-423
15. Hajela P, Lin CY (1992) Genetic search strategies in multicriterion optimal design. Struct Opt 4: 99-107
16. Hardee K et al (2004) A 0.6v 205MHz 19.5ns tRC 16Mb embedded DRAM. In: IEEE international solid-state circuits conference (ISSCC)
17. Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the first IEEE conference on evolutionary computation, IEEE World congress on computational intelligence, vol 1. IEEE Press, Piscataway, pp 82-87
18. Kharevych L, Khan R (2002) 3D Physics engine for elastic and deformable bodies http://www.cs.umd.edu/Honors/reports/kharevych.html, 2002
19. Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, Heidelberg
20. Mittal S, Risco-Martin JL, Zeigler BP (2007) DEVS-based simulation web services for net-centric T&E, In: Proceedings of the Summer computer simulation conference (SCSC 2007)
21. Panda PR et al (2001) Data and memory optimization techniques for embedded systems. ACM Trans Des Autom Electron Syst 6(2): 149-206
22. Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of 1st international conference genetic algorithms, pp 99-100
23. Shivakumar P, Jouppi NP (2006) Cacti 4.0: Integrated cache timing power-area model. Tech. Report, HP Labs
24. Van Veldhuizen DA, Zydallis JB, Lamont GB (2003) Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Trans Evolut Comput 7(2): 144-173
25. Xiong S, Li F (2003) Parallel strength pareto multi-objective evolutionary algorithm for optimization problems. In: Proceedings of the 2003 congress on evolutionary computation (CEC'2003), vol 4. IEEE Press, New York, pp 2712-2718
26. Wilson L, Moore M (2005) Cross-pollinating parallel genetic algorithms for multiobjective search and optimization. Int J Found Comput Sci 16(2): 261-280
27. Wuytack S, Catthoor F, Man HD (1996) Transforming set data types to power optimal data structures. IEEE Trans Comput Aided Des 15: 619-629
28. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans Evolut Comput 3(4): 257-271
29. Zitzler E, Laumanns M, Thiele L (2002) SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Proceedings of the evolutionary methods for design, optimization and control with application to industrial problems, pp 95-100
30. Zydallis JB, Van Veldhuizen DA, Lamont GB (2001) A statistical comparison of multiobjective evolutionary algorithms including the MOMGA-II. In: First international conference on evolutionary multi-criterion optimization, pp 226-240