Evolutionary strategies of optimization

被引：21
|
作者：
Asselmeyer, T
Ebeling, W
Rose, H
机构：
[1] Institute of Physics, Humboldt University Berlin, Berlin, D-10115
来源：
PHYSICAL REVIEW E | 1997年 / 56卷 / 01期
关键词：
D O I：
10.1103/PhysRevE.56.1171
中图分类号：
O35 [流体力学]; O53 [等离子体物理学];
学科分类号：
070204 ; 080103 ; 080704 ;
摘要：
Evolutionary algorithms have proved to be a powerful tool for solving complex optimization problems. The underlying physical and biological strategies can equally be described by a Schrodinger equation. The properties of the dynamics of optimization are encoded in the spectrum of the Hamiltonian. Analytic solutions and convergence velocity of the dynamics are calculated and compared with simulations of the corresponding algorithms. The connection between physical and biological strategies is analyzed. Mixing both strategies creates a basic class of evolutionary algorithms improving robustness and velocity of optimization.
引用
收藏
页码：1171 / 1180
页数：10
相关论文
共 50 条
  • [41] Optimization of Centrifugal Impeller Using Evolutionary Strategies and Artificial Neural Networks
    Meier, Rene
    Joos, Franz
    ADVANCES IN DATA ANALYSIS, DATA HANDLING AND BUSINESS INTELLIGENCE, 2010, : 713 - 721
  • [42] An adaptive evolutionary algorithm with coordinated selection strategies for many-objective optimization
    Gu, Qinghua
    Luo, Jiale
    Li, Xuexian
    Lu, Caiwu
    APPLIED INTELLIGENCE, 2023, 53 (08) : 9368 - 9395
  • [43] Evolutionary Extreme Learning Machine Based on Particle Swarm Optimization and Clustering Strategies
    Pacifico, Luciano D. S.
    Ludermir, Teresa B.
    2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2013,
  • [44] Optimization of Markov models with evolutionary strategies based on exact and approximate analysis techniques
    Buchholz, Peter
    Kemper, Peter
    QEST 2006: THIRD INTERNATIONAL CONFERENCE ON THE QUANTITATIVE EVALUATION OF SYSTEMS, 2006, : 233 - +
  • [45] Automatic computer optimization of casting processes using evolutionary strategies - A basic approach
    Wolf, J
    Ehlen, G
    Sahm, PR
    EUROMAT 97 - PROCEEDINGS OF THE 5TH EUROPEAN CONFERENCE ON ADVANCED MATERIALS AND PROCESSES AND APPLICATIONS: MATERIALS, FUNCTIONALITY & DESIGN, VOL 4: CHARACTERIZATION AND PRODUCTION/DESIGN, 1997, : 415 - 418
  • [46] Surrogate Models and Ensemble Strategies for Expensive Evolutionary Optimization: An Industrial Case Study
    Garza-Fabre, Mario
    Cortes-Garcia, Nicolas
    Galeana-Zapien, Hiram
    Landa, Ricardo
    IEEE ACCESS, 2024, 12 : 86144 - 86159
  • [47] Continuous Optimization with two Evolutionary Strategies: Comma (mu, lambda) and Plus (eta
    Zambrano Vega, Cristian
    Cedeno Munoz, Joel A.
    Bravo Salvatierra, Jefferson
    Diaz Ocampo, Eduardo
    REVISTA PUBLICANDO, 2016, 3 (08): : 37 - 51
  • [48] Optimization of Self-Organized Flocking of a Robot Swarm via Evolutionary Strategies
    Celikkanat, Hande
    23RD INTERNATIONAL SYMPOSIUM ON COMPUTER AND INFORMATION SCIENCES, 2008, : 144 - 147
  • [49] Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization
    Nikhil Padhye
    Pulkit Mittal
    Kalyanmoy Deb
    Computational Optimization and Applications, 2015, 62 : 851 - 890
  • [50] Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization
    Sun, Yifei
    Bian, Kun
    Liu, Zhuo
    Sun, Xin
    Yao, Ruoxia
    DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2021, 2021
12345