Add to ORKGPractical implementation methods for parallel computations in the genetic algorithm for discrete optimization, using the example of calculating dimensional chains are considered. An approach for determining the boundary dimensions of population on the basis of probability calculating for independent random events is proposed
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
The diploma thesis with the subject ¨Parallel Genetic Algorithms with Applications¨ deals with the p...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. Thi...
Many important traits in plants, animals and humans are quantitative, and most such traits are gener...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
The paper "Parallel Genetic Algorithms" discusses the theoretical basics of Evolutionary Algorithms ...
A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical ...
A parallel implementation of Genetic Programming using PVM is described. Two different topologies fo...
The paper presents an analysis of the use of optimization algorithms in parallel solutions and distr...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
The diploma thesis with the subject ¨Parallel Genetic Algorithms with Applications¨ deals with the p...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. Thi...
Many important traits in plants, animals and humans are quantitative, and most such traits are gener...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
The paper "Parallel Genetic Algorithms" discusses the theoretical basics of Evolutionary Algorithms ...
A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical ...
A parallel implementation of Genetic Programming using PVM is described. Two different topologies fo...
The paper presents an analysis of the use of optimization algorithms in parallel solutions and distr...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
The diploma thesis with the subject ¨Parallel Genetic Algorithms with Applications¨ deals with the p...