Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Neural Computation
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Bayesian Methods for Efficient Genetic Programming
Genetic Programming and Evolvable Machines
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Removing the Genetics from the Standard Genetic Algorithm
Removing the Genetics from the Standard Genetic Algorithm
Evolutionary induction of sparse neural trees
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Molecular programming: evolving genetic programs in a test tube
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A bayesian algorithm for in vitro molecular evolution of pattern classifiers
DNA'04 Proceedings of the 10th international conference on DNA computing
| Hi-index | 0.00 |
A probabilistic evolutionary framework is presented and shown to be applicable to both learning and optimization problems. In this framework, evolutionary computation is viewed as Bayesian inference that iteratively updates the posterior distribution of a population from the prior knowledge and observation of new individuals to find an individual with the maximum posterior probability. Theoretical foundations of Bayesian evolutionary computation are given and its generality is demonstrated by showing specific Bayesian evolutionary algorithms for learning and optimization. We also discuss how the probabilistic framework can be used to develop novel evolutionary algorithms that embed evolutionary learning for evolutionary optimization and vice versa.