Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for robust, efficient problem solving through highly parallel search space exploration. This work demonstrates how an improvement in performance and efficiency over the traditional serial approach can be achieved by exploiting this highly parallel nature to produce parallel genetic algorithms. Furthermore, it is shown that by incorporating domain specific knowledge into a genetic algorithm near optimal solutions can be located in minimal time
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical ...
The objective of this dissertation is to develop a multi-resolution optimization strategy based on t...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
'Evolutionary algorithms' is the collective name for a group of relatively new stochastic search alg...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]...
Abstract- In this paper we propose the implementation of a massively parallel GP model in hardware i...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical ...
The objective of this dissertation is to develop a multi-resolution optimization strategy based on t...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
'Evolutionary algorithms' is the collective name for a group of relatively new stochastic search alg...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]...
Abstract- In this paper we propose the implementation of a massively parallel GP model in hardware i...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical ...
The objective of this dissertation is to develop a multi-resolution optimization strategy based on t...