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Article

A hybrid evolutionary programming algorithm for spread spectrum radar polyphase codes design

Authors:

Angel M. Perez-Bellido,

Sancho Salcedo-Sanz,

Emilio G. Ortiz-Garcia,

Antonio Portilla-FiguerasAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Pages 682 - 688

https://doi.org/10.1145/1276958.1277100

Published: 07 July 2007 Publication History

Abstract

This paper presents a hybrid evolutionary programming algorithm to solve the spread spectrum radar polyphase code design problem. The proposed algorithm uses an Evolutionary Programming (EP) approach as global search heuristic. This EP is hybridized with a gradient-based local search procedure which includes a dynamic step adaptation procedure to perform accurate and efficient local search for better solutions. Numerical examples demonstrate that the algorithm outperforms existing approaches for this problem.

References

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Digital Library

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J. Kratica, D. Tosic, V. Filipovic and I. Ljubic, "Genetic Algorithm for Designing a Spread-Spectrum Radar Polyphase Code," In Proc. of the 5th Online World Conference on Soft Computing Methods in Industrial Applications, pp. 191--197, 2000.

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Cited By

Chen YLiang JWu YHe BLin LWang Y(2022)Self-Regulating and Self-Perception Particle Swarm Optimization with Mutation MechanismJournal of Intelligent and Robotic Systems10.1007/s10846-022-01627-y105:2Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10846-022-01627-y
Miao KMao XLi C(2019)Individualism of particles in particle swarm optimizationApplied Soft Computing10.1016/j.asoc.2019.105619(105619)Online publication date: Jul-2019
https://doi.org/10.1016/j.asoc.2019.105619
Lin ASun WYu HWu GTang H(2019)Adaptive comprehensive learning particle swarm optimization with cooperative archiveApplied Soft Computing10.1016/j.asoc.2019.01.04777(533-546)Online publication date: Apr-2019
https://doi.org/10.1016/j.asoc.2019.01.047
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Index Terms

A hybrid evolutionary programming algorithm for spread spectrum radar polyphase codes design
1. Applied computing
  1. Arts and humanities
    1. Architecture (buildings)
      1. Computer-aided design
  2. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design

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Published In

cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

Copyright © 2007 ACM.

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: 07 July 2007

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GECCO07

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

Acceptance Rates

GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Chen YLiang JWu YHe BLin LWang Y(2022)Self-Regulating and Self-Perception Particle Swarm Optimization with Mutation MechanismJournal of Intelligent and Robotic Systems10.1007/s10846-022-01627-y105:2Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10846-022-01627-y
Miao KMao XLi C(2019)Individualism of particles in particle swarm optimizationApplied Soft Computing10.1016/j.asoc.2019.105619(105619)Online publication date: Jul-2019
https://doi.org/10.1016/j.asoc.2019.105619
Lin ASun WYu HWu GTang H(2019)Adaptive comprehensive learning particle swarm optimization with cooperative archiveApplied Soft Computing10.1016/j.asoc.2019.01.04777(533-546)Online publication date: Apr-2019
https://doi.org/10.1016/j.asoc.2019.01.047
Sun WLin AYu HLiang QWu G(2017)All-dimension neighborhood based particle swarm optimization with randomly selected neighborsInformation Sciences: an International Journal10.1016/j.ins.2017.04.007405:C(141-156)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.04.007
(2016)Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problemsInformation Sciences: an International Journal10.1016/j.ins.2015.07.035326:C(1-24)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.ins.2015.07.035
Tanweer MSuresh SSundararajan N(2015)Self regulating particle swarm optimization algorithmInformation Sciences: an International Journal10.1016/j.ins.2014.09.053294:C(182-202)Online publication date: 10-Feb-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.09.053
Gil-López SSer JSalcedo-Sanz SPérez-Bellido ÁCabero JPortilla-Figueras J(2012)A hybrid harmony search algorithm for the spread spectrum radar polyphase codes design problemExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.03.06339:12(11089-11093)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1016/j.eswa.2012.03.063
Pérez-Bellido ÁSalcedo-Sanz SOrtiz-García EPortilla-Figueras JLópez-Ferreras F(2008)A comparison of memetic algorithms for the spread spectrum radar polyphase codes design problemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2008.03.01121:8(1233-1238)Online publication date: 1-Dec-2008
https://dl.acm.org/doi/10.1016/j.engappai.2008.03.011

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