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Modified differential evolution: a greedy random strategy for genetic recombination

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  • Bergey, Paul K.
  • Ragsdale, Cliff

Abstract

Over the past three decades Evolutionary Algorithms have emerged as a powerful mechanism for finding solutions to large and complex problems. A promising new evolutionary algorithm known as Differential Evolution (DE) was recently introduced and has garnered significant attention in the research literature. This paper introduces a modification to DE that enhances its rate of convergence without compromising solution quality. DE was recently shown to outperform several well-known stochastic optimization methods on an extensive set of test problems. Our Modified Differential Evolution (MDE) algorithm utilizes selection pressure to develop offspring that are more fit to survive than those generated from purely random operators. We demonstrate that MDE requires less computational effort to locate global optimal solutions to well-known test problems in the continuous domain.

Suggested Citation

  • Bergey, Paul K. & Ragsdale, Cliff, 2005. "Modified differential evolution: a greedy random strategy for genetic recombination," Omega, Elsevier, vol. 33(3), pages 255-265, June.
    • Handle: RePEc:eee:jomega:v:33:y:2005:i:3:p:255-265
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    References listed on IDEAS

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    1. Ellis, David M. & Flannery, Mark J., 1992. "Does the debt market assess large banks, risk? : Time series evidence from money center CDs," Journal of Monetary Economics, Elsevier, vol. 30(3), pages 481-502, December.
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    Cited by:

    1. Parra-López, Carlos & Groot, Jeroen C.J. & Carmona-Torres, Carmen & Rossing, Walter A.H., 2008. "Integrating public demands into model-based design for multifunctional agriculture: An application to intensive Dutch dairy landscapes," Ecological Economics, Elsevier, vol. 67(4), pages 538-551, November.
    2. Wang, Lin & He, Jing & Wu, Desheng & Zeng, Yu-Rong, 2012. "A novel differential evolution algorithm for joint replenishment problem under interdependence and its application," International Journal of Production Economics, Elsevier, vol. 135(1), pages 190-198.
    3. Liang, Wen-Yau & Huang, Chun-Che, 2008. "A hybrid approach to constrained evolutionary computing: Case of product synthesis," Omega, Elsevier, vol. 36(6), pages 1072-1085, December.
    4. J.C.J. Groot & W.H.A. Rossing & M. Tichit & N. Turpin & A. Jellema & J. Baudry & P.H. Verburg & L. Doyen & G.W.J. van de Ven, 2009. "On the contribution of modelling to multifunctional agriculture: Learning from comparisons," Post-Print hal-00455433, HAL.
    5. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.

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