On the Foundations and the Applications of Evolutionary Computing

DOI10.1007/978-3-642-32726-1_1zbMath1251.68199OpenAlexW68099401MaRDI QIDQ4649201

Emilia Tantar, Pierre Del Moral, Alexandru-Adrian Tantar

Publication date: 20 November 2012

Published in: EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-32726-1_1

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Signal detection and filtering (aspects of stochastic processes) (60G35) Applications of branching processes (60J85) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Cites Work

• Optimization by Simulated Annealing
• On adaptive resampling strategies for sequential Monte Carlo methods
• Concentration inequalities for mean field particle models
• Sequential Monte Carlo for rare event estimation
• A nonasymptotic theorem for unnormalized Feynman-Kac particle models
• Correlated and uncorrelated fitness landscapes and how to tell the difference
• Reactive search and intelligent optimization
• Genetic algorithms and very fast simulated reannealing: A comparison
• Measure-valued processes and interacting particle systems. Application to nonlinear filtering problems
• Simulated annealing: Practice versus theory
• Fast local search and guided local search and their application to British Telecom's workforce scheduling problem
• Variable neighborhood search
• Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
• Annealed Feynman-Kac models
• A Moran particle system approximation of Feynman-Kac formulae
• On the stability of nonlinear Feynman-Kac semigroups
• Hoeffding's inequality for uniformly ergodic Markov chains
• Future paths for integer programming and links to artificial intelligence
• Genealogies and increasing propagation of chaos for Feynman-Kac and genetic models.
• Iterative methods for solving linear equations
• Sequential Monte Carlo Methods in Practice
• Multilevel Splitting for Estimating Rare Event Probabilities
• On the Concentration Properties of Interacting Particle Processes
• Particle Motions in Absorbing Medium with Hard and Soft Obstacles
• Evolutionary Computation for Modeling and Optimization
• Sequential Monte Carlo Samplers
• New Ways to Calibrate Evolutionary Algorithms
• Evolutionary Algorithms on Volunteer Computing Platforms: The MilkyWay@Home Project
• `` Direct Search Solution of Numerical and Statistical Problems
• Updating Quasi-Newton Matrices with Limited Storage
• Minimization by Random Search Techniques
• Asymptotic convergence of genetic algorithms
• Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
• Particle approximations of Lyapunov exponents connected to Schrödinger operators and Feynman–Kac semigroups
• A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study
• A sequential particle filter method for static models
• On the stability of measure valued processes with applications to filtering
• Equation of State Calculations by Fast Computing Machines
• A Rapidly Convergent Descent Method for Minimization
• Function minimization by conjugate gradients
• A Family of Variable-Metric Methods Derived by Variational Means
• The Convergence of a Class of Double-rank Minimization Algorithms
• A new approach to variable metric algorithms
• On the Convergence of the Variable Metric Algorithm
• Conditioning of Quasi-Newton Methods for Function Minimization
• A Simplex Method for Function Minimization
• Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation
• The Monte Carlo Method
• Methods of conjugate gradients for solving linear systems
• Introduction to evolutionary computing
• On the stability of interacting processes with applications to filtering and genetic algorithms
• Monte Carlo strategies in scientific computing
• The Monte-Carlo method for filtering with discrete-time observations
• A survey of optimization by building and using probabilistic models
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: On the Foundations and the Applications of Evolutionary Computing