Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead
 Giovanni Iacca,
 Fabio Caraffini,
 Ferrante Neri
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Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of populationbased algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithms have a similar behaviour with respect to populationbased algorithms but require a much smaller memory. This feature is crucially important in some engineering applications, especially in robotics. A high performance compact algorithm is the compact Differential Evolution (cDE) algorithm. This paper proposes a novel implementation of cDE, namely compact Differential Evolution light (cDElight), to address not only the memory saving necessities but also realtime requirements. cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss, with respect to cDE. Numerical results, carried out on a broad set of test problems, show that cDElight, despite its minimal hardware requirements, does not deteriorate the performance of cDE and thus is competitive with other memory saving and populationbased algorithms. An application in the field of mobile robotics highlights the usability and advantages of the proposed approach.
 Norman, PG (1987) The new AP101S generalpurpose computer (GPC) for the space shuttle. IEEE Proceedings 75: pp. 308319 CrossRef
 Qin, AK, Huang, VL, Suganthan, PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation 13: pp. 398417 CrossRef
 Zhang, J, Sanderson, AC (2009) JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13: pp. 945958 CrossRef
 Handoko, SD, Kwoh, CK, Ong, YS (2010) Feasibility structure modeling: An effective chaperon for constrained memetic algorithms. IEEE Transactions on Evolutionary Computation 14: pp. 740758 CrossRef
 PrügelBennet, A (2010) Benefits of a population: Five mechanisms that advantage populationbased algorithms. IEEE Transactions on Evolutionary Computation 14: pp. 500517 CrossRef
 Larrañaga P, Lozano J A. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic, 2002.
 Harik, GR, Lobo, FG, Goldberg, DE (1999) The compact genetic algorithm. IEEE Transactions on Evolutionary Computation 3: pp. 287297 CrossRef
 Rastegar, R, Hariri, A (2006) A step forward in studying the compact genetic algorithm. Evolutionary Computation 14: pp. 277289 CrossRef
 Hidalgo J I, Prieto M, Lanchares J et al. Hybrid parallelization of a compact genetic algorithm. In Proc. the 11th Euromicro Conference on Parallel, Distributed and NetworkBased Processing, Feb. 2003, pp.449–445.
 Lobo F G, Lima C F, Mártires H. An architecture for massive parallelization of the compact genetic algorithm. In Lecture Notes in Computer Science 3103, Deb K, Poli R, Banzhaf W, et al. (eds.), Springer, 2004, pp.412–413.
 Harik G. Linkage learning via probabilistic modeling in the ECGA. Tech. Rep. 99010, University of Illinois at UrbanaChampaign, Urbana, IL, 1999.
 Harik G R, Lobo F G, Sastry K. Linkage learning via probabilistic modeling in the extended compact genetic algorithm (ECGA). In Proc. Scalable Optimization via Probabilistic Modeling, 33, Pelikan M, Sastry K, CantúPaz E (eds.), Springer, 2006, pp.3961.
 Sastry K, Goldberg D E. On extended compact genetic algorithm. Tech. Rep. 2000026, University of Illinois at UrbanaChampaign, Urbana, IL, 2000.
 Sastry K, Xiao G. Cluster optimization using extended compact genetic algorithm. Tech. Rep. 2001016, University of Illinois at UrbanaChampaign, Urbana, IL, 2001.
 Ahn, CW, An, J, Yoo, JC (2012) Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs. Information Sciences 192: pp. 109119 CrossRef
 Sastry, K, Goldberg, DE, Johnson, DD (2007) Scalability of a hybrid extended compact genetic algorithm for ground state optimization of clusters. Materials and Manufacturing Processes 22: pp. 570576 CrossRef
 Aporntewan C, Chongstitvatana P. A hardware implementation of the compact genetic algorithm. In Proc. the IEEE Congress on Evolutionary Computation, May 2001, pp.624–629.
 Gallagher, JC, Vigraham, S, Kramer, G (2004) A family of compact genetic algorithms for intrinsic evolvable hardware. IEEE Transactions Evolutionary Computation 8: pp. 111126 CrossRef
 Jewajinda Y, Chongstitvatana P. Cellular compact genetic algorithm for evolvable hardware. In Proc. the 5th Int. Conf. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, May 2008, pp.1–4.
 Gallagher J C, Vigraham S. A modified compact genetic algorithm for the intrinsic evolution of continuous time recurrent neural networks. In Proc. the Genetic and Evolutionary Computation Conference, July 2002, pp.163–170.
 Ahn, CW, Ramakrishna, RS (2003) Elitismbased compact genetic algorithms. IEEE Transactions on Evolutionary Computation 7: pp. 367385 CrossRef
 Rudolph G. A partial order approach to noisy fitness functions. In Proc. the IEEE Congress on Evolutionary Computation, May 2001, pp.318–325.
 Mininno, E, Cupertino, F, Naso, D (2008) Realvalued compact genetic algorithms for embedded microcontroller optimization. IEEE Transactions on Evolutionary Computation 12: pp. 203219 CrossRef
 Cupertino F, Mininno E, Naso D. Elitist compact genetic algorithms for induction motor selftuning control. In Proc. the IEEE Congress on Evolutionary Computation, July 2006, pp.3057–3063.
 Cupertino F, Mininno E, Naso D. Compact genetic algorithms for the optimization of induction motor cascaded control. In Proc. the IEEE International Conference on Electric Machines and Drives, May 2007, pp.82–87.
 Mininno, E, Neri, F, Cupertino, F, Naso, D (2011) Compact differential evolution. IEEE Transactions on Evolutionary Computation 15: pp. 3254 CrossRef
 Neri, F, Tirronen, V (2010) Recent advances in differential evolution: A review and experimental analysis. Artificial Intelligence Review 33: pp. 61106 CrossRef
 Neri, F, Mininno, E (2010) Memetic compact differential evolution for cartesian robot control. IEEE Computational Intelligence Magazine 5: pp. 5465 CrossRef
 Neri, F, Iacca, G, Mininno, E (2011) Disturbed exploitation compact differential evolution for limited memory optimization problems. Information Sciences 181: pp. 24692487 CrossRef
 Iacca G, Mallipeddi R, Mininno E, Neri F, Suganthan P N. Superfit and population size reduction in compact differential evolution. In Proc. IEEE Symposium on Memetic Computing, April 2011, pp.1–8.
 Iacca G, Neri F, Mininno E. Oppositionbased learning in compact differential evolution. In Lecture Notes in Computer Science 6624, Di Chio C, Cagnoni S, Cotta C, et al. (eds.), Springer, 2011, pp.264–273.
 Caponio, A, Neri, F, Tirronen, V (2009) Superfit control adaptation in memetic differential evolution frameworks. Soft ComputingA Fusion of Foundations, Methodologies and Applications 13: pp. 811831
 Rahnamayan, S, Tizhoosh, HR, Salama, MM (2008) Oppositionbased differential evolution. IEEE Transactions on Evolutionary Computation 12: pp. 6479 CrossRef
 Iacca, G, Mininno, E, Neri, F (2011) Composed compact differential evolution. Evolutionary Intelligence 4: pp. 1729 CrossRef
 Iacca G, Mallipeddi R, Mininno E et al. Global supervision for compact differential evolution. In Proc. IEEE Symp. Differential Evolution, April 2011, pp.25–32.
 Mallipeddi R, Iacca G, Suganthan P N, Neri F, Mininno E. Ensemble strategies in compact differential evolution. In Proc. the IEEE Congress on Evolutionary Computation, June 2011, pp.1972–1977.
 Das, S, Suganthan, PN (2011) Differential evolution: A survey of the stateoftheart. IEEE Transactions on Evolutionary Computation 15: pp. 431 CrossRef
 Price K V, Storn R, Lampinen J. Differential Evolution: A Practical Approach to Global Optimization, Springer, 2005.
 Gautschi W. Error function and fresnel integrals. In Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, Abramowitz M, Stegun I A (eds.), 1972, pp.297–309.
 Cody, WJ (1969) Rational chebyshev approximations for the error function. Mathematics of Computation 23: pp. 631637 CrossRef
 Chen H, Zhu Y, Hu K. Adaptive bacterial foraging optimization. Abstract and Applied Analysis, 2011, Article ID 108269.
 Das, S, Abraham, A, Chakraborty, UK (2009) Differential evolution with a neighborhoodbased mutation operator. IEEE Trans. Evolutionary Computation 13: pp. 526553 CrossRef
 Auger A, Hansen N. A restart CMA evolution strategy with increasing population size. In Proc. the IEEE Congress on Evolutionary Computation, Sept. 2005, pp.1769–1776.
 Zhou J, Ji Z, Shen L. Simplified intelligence single particle optimization based neural network for digit recognition. In Proc. the Chinese Conference on Pattern Recognition, Oct. 2008.
 Zhao, X (2011) Simulated annealing algorithm with adaptive neighborhood. Applied Soft Computing 11: pp. 18271836 CrossRef
 Hansen N, Auger A, Finck S, Ros R. Realparameter blackbox optimization benchmarking 2010: Experimental setup. Tech. Rep. RR7215, INRIA, 2010.
 Tang K, Yao X, Suganthan P N, MacNish C, Chen Y P, Chen C M, Yang Z. Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Tech. Rep., Nature Inspired Computation and Applications Laboratory, USTC, China, 2007.
 Wilcoxon, F (1945) Individual comparisons by ranking methods. Biometrics Bulletin 1: pp. 8083 CrossRef
 Holm, S (1979) A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6: pp. 6570
 Garcia, S, Fernandez, A, Luengo, J, Herrera, F (2008) A study of statistical techniques and performance measures for geneticsbased machine learning: Accuracy and interpretability. Soft Computing 13: pp. 959977 CrossRef
 Bräuni T. Embedded Robotics: Mobile Robot Design and Applications with Embedded Systems (3rd edition), Springer, 2008.
 Mindstorms education, NXT User Guide, 2006, http://education.lego.com/downloads/?q=f02FB6AC107BO4E1A862D7AE2DBC88F9Eg , Aug. 2011.
 Title
 Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead
 Journal

Journal of Computer Science and Technology
Volume 27, Issue 5 , pp 10561076
 Cover Date
 20120901
 DOI
 10.1007/s1139001212842
 Print ISSN
 10009000
 Online ISSN
 18604749
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 differential evolution
 compact optimization
 realtime optimization
 Industry Sectors
 Authors

 Giovanni Iacca ^{(1)}
 Fabio Caraffini ^{(1)}
 Ferrante Neri ^{(1)}
 Author Affiliations

 1. Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35 (Agora), 40014, Jyväskylä, Finland