A new genetic algorithm
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Publication:2564690
DOI10.1214/aoap/1034968228zbMath0860.60017OpenAlexW1976880484MaRDI QIDQ2564690
Publication date: 13 April 1997
Published in: The Annals of Applied Probability (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aoap/1034968228
Problems related to evolution (92D15) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Large deviations (60F10)
Related Items (9)
Global optimization with exploration/selection algorithms and simulated annealing ⋮ Meta-control of an interacting-particle algorithm for global optimization ⋮ The elitist non-homogeneous genetic algorithm: almost sure convergence ⋮ Dynamiques recuites de type Feynman-Kac : résultats précis et conjectures ⋮ Mean Convergence Time of Inhomogeneous Genetic Algorithm with Elitism ⋮ The interacting-particle algorithm with dynamic heating and cooling ⋮ Geometric Convergence of Genetic Algorithms Under Tempered Random Restart ⋮ The exit path of a Markov chain with rare transitions ⋮ A new genetic algorithm specifically based on mutation and selection
Cites Work
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- Efficient simulated annealing on fractal energy landscapes
- Asymptotics of the spectral gap with applications to the theory of simulated annealing
- Simulated annealing via Sobolev inequalities
- Sharp large deviations estimates for simulated annealing algorithms
- Applications of sharp large deviations estimates to optimal cooling schedules
- Singular perturbed Markov chains and exact behaviors of simulated annealing processes
- Rough large deviation estimates for simulated annealing: Application to exponential schedules
- Simulated annealing - to cool or not
- \(L^ 2\) convergence of time nonhomogeneous Markov processes. I: Spectral estimates
- Asymptotical behaviour of several interacting annealing processes
- Rough large deviation estimates for the optimal convergence speed exponent of generalized simulated annealing algorithms
- The dynamics of mutation-selection algorithms with large population sizes
- Polynomial-Time Approximation Algorithms for the Ising Model
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Convergence of an annealing algorithm
- Cooling Schedules for Optimal Annealing
- Note on the Convergence of Simulated Annealing Algorithms
- Large Cliques Elude the Metropolis Process
- Improved Bounds for Mixing Rates of Markov Chains and Multicommodity Flow
- The time complexity of maximum matching by simulated annealing
- Cycle Decompositions and Simulated Annealing
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