Main Article Content

Anderson Sergio
Federal University of Pernambuco
Brazil
Sidartha Carvalho
Federal University of Pernambuco
Brazil
Marco Rego
Federal University of Pernambuco
Brazil
Vol. 3 No. 4 (2014), Articles, pages 13-23
DOI: https://doi.org/10.14201/ADCAIJ2014341323
Accepted: Oct 5, 2015
Copyright

Abstract

Compact evolutionary algorithms have proven to be an efficient alternative for solving optimization problems in computing environments with low processing power. In this kind of solution, a probability distribution simulates the behavior of a population, thus looking for memory savings. Several compact algorithms have been proposed, including the compact genetic algorithm and compact differential evolution. This work aims to investigate the use of compact approaches in other important evolutionary algorithms: evolution strategies. This paper proposes two different approaches for compact versions of evolution strategies. Experiments were performed and the results analyzed. The results showed that, depending on the nature of problem, the use of the compact version of Evolution Strategies can be rewarding.

Downloads

Download data is not yet available.

Article Details

References

Aporntewan, Chatchawit; Chongstitvatana, Prabhas. A hardware implementation of the compact genetic algorithm. In: IEEE Congress on Evolutionary Computation. 2001. pp. 624-629.

http://dx.doi.org/10.1109/cec.2001.934449

Baraglia, Ranieri; Hidalgo, Jose Ignacio; Perego, Raffaele. A hybrid heuristic for the traveling salesman problem. Evolutionary Computation, IEEE Transactions on, v. 5, n. 6, pp. 613-622, 2001.

Carroll, Aaron; Heiser, Gernot. An Analysis of Power Consumption in a Smartphone. In: Proceedings of the USENIX Annual Technical Conference. 2010.

Efron, Bradley. Student's t-test under symmetry conditions. Journal of the American Statistical Association, v. 64, n. 328, p. 1278-1302, 1969.

http://dx.doi.org/10.2307/2286068

Eiben, Agoston E.; SMITH, James E. Introduction to evolutionary computing. Springer Science & Business Media, 2003.

http://dx.doi.org/10.1007/978-3-662-05094-1

Hansen, Nikolaus. The CMA evolution strategy: a comparing review. In: Towards a new evolutionary computation. Springer Berlin Heidelberg, 2006. pp. 75-102.

http://dx.doi.org/10.1007/3-540-32494-1_4

Harik, Georges R.; LOBO, Fernando G.; GOLDBERG, David E. The compact genetic algorithm. Evolutionary Computation, IEEE Transactions on, v. 3, n. 4, pp. 287-297, 1999.

Holland, John H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press, 1975.

IDC. Intelligent Systems to Exceed $1 Trillion in 2019 as the Market Continues to Disrupt Traditional Industries Including Manufacturing, Energy, and Transportation. Avaliable in: < http://www.idc.com/getdoc.jsp?containerId=prUS25204914>. Accessed on 2014, October 16th.

Jewajinda, Yutana; Chongstitvatana, Prabhas. Cellular compact genetic algorithm for evolvable hardware. In: Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on. IEEE, 2008. pp. 1-4.

http://dx.doi.org/10.1109/ecticon.2008.4600358

Larraga-a, Pedro; Lozano, Jose A. (Ed.). Estimation of distribution algorithms: A new tool for evolutionary computation. Springer Science & Business Media, 2002.

http://dx.doi.org/10.1007/978-1-4615-1539-5

Matsumura, Yoshiyuki; Sugiyama, Kiyotaka; Yasuda, Toshiyuki; Ohkura, Kazuhiro. Evolutionary and Particle Swarm Hybrid Algorithm over Cloud Computing, with an Application to Dinosaur Gait Optimization. Proceedings of the IEEE/SICE International Symposium on System Integration, Kobe, Japan, December, 2013.

http://dx.doi.org/10.1109/sii.2013.6776759

Mininno, Ernesto et al. Compact differential evolution. Evolutionary Computation, IEEE Transactions on, v. 15, n. 1, pp. 32-54, 2011.

Neri, Ferrante; Mininno, Ernesto; Iacca, Giovanni. Compact Particle Swarm Optimization. Information Sciences: an International Journal, 239, p.96-121, August, 2013.

Paradiso, Joseph A.; Starner, Thad. Energy scavenging for mobile and wireless electronics. Pervasive Computing, IEEE, v. 4, n. 1, pp. 18-27, 2005.

http://dx.doi.org/10.1109/MPRV.2005.9

Sastry, Kumara; Goldberg, David E. On extended compact genetic algorithm. In: Late-Breaking Paper at the Genetic and Evolutionary Computation Conference. 2000. pp. 352-359.

Sastry, Kumara; Xiao, Guanghua. Cluster Optimization Using Extended Compact Genetic Algorithm. Urbana, v. 51, p. 61801, 1989.

Sastry, Kumara; Goldberg, David E.; Johnson, D. D. Scalability of a hybrid extended compact genetic algorithm for ground state optimization of clusters. Materials and Manufacturing Processes, v. 22, n. 5, pp. 570-576, 2007.

http://dx.doi.org/10.1080/10426910701319654

Optimizations Functions. Definition of the Optimization Functions. Available in: < http://www.sfu.ca/~ssurjano/optimization.html >. Accessed on 2015, June 10th.

Shwefel, H. P. Numerische Optimierung von Computer-Modellen. PhD Thesis, 1974. Decision support methods for global optimization.