Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Applied numerical linear algebra
Applied numerical linear algebra
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Theory of evolutionary algorithms: a bird's eye view
Theoretical Computer Science - Special issue on evolutionary computation
On the convergence rates of genetic algorithms
Theoretical Computer Science - Special issue on evolutionary computation
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Hybrid evolutionary algorithm for solving optimization problems
International Journal of Computer Mathematics - Celebrating the Life of David J. Evans
A Steep Thermodynamical Selection Rule for Evolutionary Algorithms
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
A Novel Multi-objective Evolutionary Algorithm
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
A particle gradient evolutionary algorithm based on statistical mechanics and convergence analysis
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Exploiting molecular dynamics for multi-objective optimization
Expert Systems with Applications: An International Journal
A new dynamic particle swarm optimizer
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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In this paper, a new dynamical evolutionary algorithm (DEA) is presented based on the theory of statistical mechanics. The novelty of this kind of dynamical evolutionary algorithm is that all individuals in a population (called particles in a dynamical system) are running and searching with their population evolving driven by a new selecting mechanism. This mechanism simulates the principle of molecular dynamics, which is easy to design and implement. A basic theoretical analysis for the dynamical evolutionary algorithm is given and as a consequence two stopping criteria of the algorithm are derived from the principle of energy minimization and the law of entropy increasing. In order to verify the effectiveness of the scheme, DEA is applied to solving some typical numerical function minimization problems which are poorly solved by traditional evolutionary algorithms. The experimental results show that DEA is fast and reliable.