Add to ORKGIn this article an experience of the utilization of PRC (Public Resource Computation) in research projects that needs large quantities of CPU time is presented. We have developed a distributed architecture based on middleware BOINC and LilGP Genetic Programming tool. In order to run LilGP applications under BOINC platforms, some core LilGP functions has been adapted to BOINC requirements. We have used a classic GP problem known as the artificial ANT in Santa Fe Trail. Some computers from a classroom were used acting as clients, proving that they can be used for scientific computation in conjunction with their primary uses
We describe our experimentation with the design and implementation of specific environments, consist...
Abstract- In this paper we propose the implementation of a massively parallel GP model in hardware i...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Techniques of evolutionary computation generally require significant computational resources to solv...
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
As modern research relies more and more on computers, computer cycles are becoming a scarce resource...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
Genetic Programming is an evolutionary computation technique which searches for those computer progr...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
Linear Genetic Programming (LGP) is a powerful problem-solving technique, but one with several signi...
Linear Genetic Programming (LGP) is a powerful problem-solving technique, but one with several signi...
The recent development of the Genetic and Evolutionary Computation field lead to a kaleidoscope of a...
Genetic Algorithms (GAs) are a class of numerical and combinatorial optimisers which are especially ...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...
We describe our experimentation with the design and implementation of specific environments, consist...
Abstract- In this paper we propose the implementation of a massively parallel GP model in hardware i...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Techniques of evolutionary computation generally require significant computational resources to solv...
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
As modern research relies more and more on computers, computer cycles are becoming a scarce resource...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
Genetic Programming is an evolutionary computation technique which searches for those computer progr...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
Linear Genetic Programming (LGP) is a powerful problem-solving technique, but one with several signi...
Linear Genetic Programming (LGP) is a powerful problem-solving technique, but one with several signi...
The recent development of the Genetic and Evolutionary Computation field lead to a kaleidoscope of a...
Genetic Algorithms (GAs) are a class of numerical and combinatorial optimisers which are especially ...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...
We describe our experimentation with the design and implementation of specific environments, consist...
Abstract- In this paper we propose the implementation of a massively parallel GP model in hardware i...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...