Many optimization problems have complex search space, which either increase the solving problem time or finish searching without obtaining the best solution. Genetic Algorithm (GA) is an optimization technique used in solving many practical problems in science, engineering, and business domains. Parallel Genetic Algorithm (PGA) has been widely used to increase speed of GA, especially after the spread of parallel platforms such as GPUs, FPGA, and Multi-Core Processors. In this paper, we introduce a type of PGA called Fine-grained Parallel Genetic Algorithm, which has the advantages of maintaining better population diversity, and inhibiting premature. Fine-grained PGA is implemented on graphics hardware
Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing ...
Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. Thi...
A genetic algorithm (GA) is an optimization method based on natural selection. Genetic algorithms ha...
© 2014 Technical University of Munich (TUM).Parallel genetic algorithms (pGAs) are a variant of gene...
The field of FPGA design is ever-growing due to costs being lower than that of ASICs, as well as the...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Uni...
A new coarse grain parallel genetic algorithm (PGA) and a new implementation of a data-parallel GA a...
migration strategy; Abstract. Genetic Algorithm (GA) is a powe rful global optimization search algo ...
Genetic algorithms are frequently used to solve optimization problems. However, the problems become ...
In this paper, we report a parallel Hybrid Genetic Algorithm (HGA) on consumer-level graphics cards....
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
The PgaFrame offers to non-expert users the possibility to solve optimization problems via genetic a...
In this research, we have implemented a parallel EP on consumer-level graphics processing units and ...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing ...
Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. Thi...
A genetic algorithm (GA) is an optimization method based on natural selection. Genetic algorithms ha...
© 2014 Technical University of Munich (TUM).Parallel genetic algorithms (pGAs) are a variant of gene...
The field of FPGA design is ever-growing due to costs being lower than that of ASICs, as well as the...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Uni...
A new coarse grain parallel genetic algorithm (PGA) and a new implementation of a data-parallel GA a...
migration strategy; Abstract. Genetic Algorithm (GA) is a powe rful global optimization search algo ...
Genetic algorithms are frequently used to solve optimization problems. However, the problems become ...
In this paper, we report a parallel Hybrid Genetic Algorithm (HGA) on consumer-level graphics cards....
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
The PgaFrame offers to non-expert users the possibility to solve optimization problems via genetic a...
In this research, we have implemented a parallel EP on consumer-level graphics processing units and ...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing ...
Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. Thi...
A genetic algorithm (GA) is an optimization method based on natural selection. Genetic algorithms ha...