In the recent years we introduced a continuity operator, the "Superindividual", that allows for the inclusion of knowledge in the evolution of the Genetic Algorithm, since we deal with very complex optimization problems, we developed a Parallel Genetic Algorithm, with the Superindividual operator. The paper presents this parallel algorithm, which improves on the results of the conventional Genetic Algorithm. Two different models of Parallel Genetic Algorithms are compared, the results are very encouraging
In this paper we address the physical parallelization of a very efficient genetic algorithm (GA) kno...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
Lecture #1: From Evolution Theory to Evolutionary Computation. Evolutionary computation is a subfiel...
The parallel genetic algorithm (PGA) uses two major modifications compared to the genetic algorithm....
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
The paper "Parallel Genetic Algorithms" discusses the theoretical basics of Evolutionary Algorithms ...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
Evolutionary algorithms have been gaining increased attention the past few years because of their ve...
Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. ...
In this paper we address the physical parallelization of a very efficient genetic algorithm (GA) kno...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
Lecture #1: From Evolution Theory to Evolutionary Computation. Evolutionary computation is a subfiel...
The parallel genetic algorithm (PGA) uses two major modifications compared to the genetic algorithm....
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
The paper "Parallel Genetic Algorithms" discusses the theoretical basics of Evolutionary Algorithms ...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
Evolutionary algorithms have been gaining increased attention the past few years because of their ve...
Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. ...
In this paper we address the physical parallelization of a very efficient genetic algorithm (GA) kno...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...