The Brandeis dice problem, originally introduced in 1962 by Jaynes as an illustration of the principle of maximum entropy, was solved using the genetic algorithm, and the resulting solution was compared with that obtained analytically. The effect of varying the genetic algorithm parameters was observed, and the optimum values for population size, mutation rate, and mutation interval were determined for this problem. The optimum genetic algorithm program was then compared to a completely random method of search and optimization. Finally, the genetic algorithm approach was extended to several variations of the original problem for which an analytical approach would be impractical
This is an electronic version of the paper presented at The European Simulation and Modelling Confer...
Tihis article is posted here with permission from the IEEE - Copyright @ 2006 IEEEUsing diploidy and...
Use of non-deterministic algorithms for solving multi-variable optimization problems is widely used ...
In the field of optimization using probabilistic models of the search space, this thesis identifies ...
Estimation of Distribution Algorithms EDA have been proposed as an extension of genetic algorithms. ...
The dynamic complexity of time series of natural phenomena allowed to improve the performance of the...
University of Technology, Sydney. Faculty of Engineering and Information Technology.Evolutionary alg...
The objectives of this research are to develop a predictive theory of the Breeder Genetic Algorithm ...
A formalism for modelling the dynamics of Genetic Algorithms (GAs) using methods from statistical me...
This thesis is concerned with the application of genetic algorithms to solve the p-median problem. T...
Abstract—Estimation of distribution algorithms sample new solutions (offspring) from a probability m...
Genetic algorithms provide an alternative to traditional optimization techniques by using directed r...
Genetic algorithms are search techniques that borrow ideas from the biological process of evolution....
A genetic algorithm (GA) is a meta-heuristic computation method that is inspired by Darwin's theory ...
Abstract — Genetic algorithm (GA), as an important intelligence computing tool, is a wide research c...
This is an electronic version of the paper presented at The European Simulation and Modelling Confer...
Tihis article is posted here with permission from the IEEE - Copyright @ 2006 IEEEUsing diploidy and...
Use of non-deterministic algorithms for solving multi-variable optimization problems is widely used ...
In the field of optimization using probabilistic models of the search space, this thesis identifies ...
Estimation of Distribution Algorithms EDA have been proposed as an extension of genetic algorithms. ...
The dynamic complexity of time series of natural phenomena allowed to improve the performance of the...
University of Technology, Sydney. Faculty of Engineering and Information Technology.Evolutionary alg...
The objectives of this research are to develop a predictive theory of the Breeder Genetic Algorithm ...
A formalism for modelling the dynamics of Genetic Algorithms (GAs) using methods from statistical me...
This thesis is concerned with the application of genetic algorithms to solve the p-median problem. T...
Abstract—Estimation of distribution algorithms sample new solutions (offspring) from a probability m...
Genetic algorithms provide an alternative to traditional optimization techniques by using directed r...
Genetic algorithms are search techniques that borrow ideas from the biological process of evolution....
A genetic algorithm (GA) is a meta-heuristic computation method that is inspired by Darwin's theory ...
Abstract — Genetic algorithm (GA), as an important intelligence computing tool, is a wide research c...
This is an electronic version of the paper presented at The European Simulation and Modelling Confer...
Tihis article is posted here with permission from the IEEE - Copyright @ 2006 IEEEUsing diploidy and...
Use of non-deterministic algorithms for solving multi-variable optimization problems is widely used ...