GAHC: Improved Genetic Algorithm

被引:0
|
作者
Matousek, Radomil [1 ]
机构
[1] Brno Univ Technol, Fac Mech Engn, Dept Appl Comp Sci, Brno 61669, Czech Republic
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper introduces a novel improved evolutionary algorithm, which combines genetic algorithms and hill climbing. Genetic Algorithms (GA) belong to a class of well established optimization meta-heuristics and their behavior are studied and analyzed in great detail. Various modifications were proposed by different researchers, for example modifications to the mutation operator. These modifications usually change the overall behavior of the algorithm. This paper presents a binary GA with a modified mutation operator, which is based on the well-known Hill Climbing Algorithm (HCA). The resulting algorithm, referred to as GAHC, also uses an elite tournament selection operator. This selection operator preserves the best individual from the GA population during the selection process while maintaining the positive characteristics of the standard tournament selection. This paper discusses the GAHC algorithm and compares its performance with standard GA.
引用
收藏
页码:507 / 520
页数:14
相关论文
共 50 条
  • [41] An Improved Genetic Algorithm and Its Application
    Wang, Mingdong
    Liu, Xianlin
    Yu, Jilai
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 8642 - +
  • [42] An Improved Genetic Algorithm for Power Grid
    Zhu, Youchan
    Guo, Xueying
    Li, Jing
    FIFTH INTERNATIONAL CONFERENCE ON INFORMATION ASSURANCE AND SECURITY, VOL 1, PROCEEDINGS, 2009, : 455 - 458
  • [43] Improved crossover operator of genetic algorithm
    Lu, Hou-Qing
    Chen, Liang
    Song, Yi-Sheng
    Wu, Zhi-Min
    Zou, Yun-Bo
    Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of Science and Technology (Natural Science Edition), 2007, 8 (03): : 250 - 253
  • [44] Application of the improved quantum genetic algorithm
    Xu, Yufa, 1600, Springer Verlag (462):
  • [45] OVRPSTW and Its Improved Genetic Algorithm
    Duan Fenghua
    ADVANCED TECHNOLOGY IN TEACHING - PROCEEDINGS OF THE 2009 3RD INTERNATIONAL CONFERENCE ON TEACHING AND COMPUTATIONAL SCIENCE (WTCS 2009), VOL 2: EDUCATION, PSYCHOLOGY AND COMPUTER SCIENCE, 2012, 117 : 249 - 255
  • [46] An Improved Genetic Algorithm Based on Triangulation
    Liu, Guangyuan
    Li, Xuedong
    Wang, Shuxin
    Ma, Yongqiang
    2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1, 2009, : 447 - 451
  • [47] IMGA: Improved Microbial Genetic Algorithm
    Liu, Yifei
    Gao, Yankun
    Liu, Yang
    2022 IEEE 22ND INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY, AND SECURITY COMPANION, QRS-C, 2022, : 189 - 192
  • [48] An Improved Crossover Operator of Genetic Algorithm
    Zhang Qi-yi
    Chang Shu-chun
    SECOND INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN, VOL 2, PROCEEDINGS, 2009, : 82 - 86
  • [49] Initialization method of genetic algorithm based on improved clustering algorithm
    Li, Hao
    Jiang, Xuesong
    Wei, Xiumei
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 447 - 450
  • [50] An improved OTSU method based on Genetic Algorithm
    Shang, Wei
    Cheng, Yan-fen
    Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications, 2016, 71 : 1656 - 1661