On the convergence rate of the elitist genetic algorithm based on mutation probability
From MaRDI portal
Publication:5085610
DOI10.1080/03610926.2018.1528361OpenAlexW2995532778MaRDI QIDQ5085610
Unnamed Author, Roberto T. G. de Oliveira, Carla A. Vivacqua, Viviane S. M. Campos, André G. C. Pereira
Publication date: 27 June 2022
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2018.1528361
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Cites Work
- Unnamed Item
- Unnamed Item
- Markov chains and stochastic stability
- On the genetic algorithm with adaptive mutation rate and selected statistical applications
- Bayesian optimal sequential design for nonparametric regression via inhomogeneous evolutionary MCMC
- Quantitative bounds for convergence rates of continuous time Markov processes
- Rates of convergence of the Hastings and Metropolis algorithms
- Optimization of laminated composite plates for maximum fundamental frequency using elitist-genetic algorithm and finite strip method
- On the convergence rates of genetic algorithms
- Non-negative matrices and Markov chains.
- Ergodicity Coefficients Defined by Vector Norms
- Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
- Geometric Convergence Rates for Stochastically Ordered Markov Chains
- Modeling the Genetic Algorithm by a Nonhomogeneous Markov Chain: Weak and Strong Ergodicity
This page was built for publication: On the convergence rate of the elitist genetic algorithm based on mutation probability