Cooling schedules for optimal annealing
Mathematics of Operations Research
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Markov chains with rare transitions and simulated annealing
Mathematics of Operations Research
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Optimization Using Neural Networks
IEEE Transactions on Computers - Special issue on artificial neural networks
Capacity of associative memory using a nonmonotonic neuron model
Neural Networks
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Asymmetric Hopfield-type networks: theory and applications
Neural Networks
Cycle Decompositions and Simulated Annealing
SIAM Journal on Control and Optimization
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Network for Optimization and Combinatorics
Neural Network for Optimization and Combinatorics
The state of play in machine/environment interactions
Artificial Intelligence Review
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The aim of this paper is twofold. First, we shall focus on Lyapunov functions for discrete dynamical systems. We shall propose a methodology for building Lyapunov functions. This methodology will be based upon the introduction of small random perturbations in the deterministic dynamics. Then we shall deal with concentration results for the perturbed dynamics. Our ultimate goal is to force the convergence of the perturbed process towards a set of specified attractors of the deterministic system. We shall illustrate our results on the paradigms of global minimization and associative memory. Our formalism will be illustrated on new algorithms for which the asymptotic analysis can be done rigorously.