Add to ORKG[[abstract]]Genetic programming (GP) is inspired by the popular genetic algorithm (GA). The searching result of GP is a program that includes both opera-tors and operands. The operators are the obstacle to the crossover and mutation process because invalid programs would be generated. In this paper, the concepts of VLIW is incorporated in the design of a GP scheme. A program in the proposed scheme is represented using only operands. The simulation results show that this approach is feasible and the performance could be increased by the instruction-level parallelism of the VLIW structure.[[notice]]補正完
It is approximately 50 years since the first computational experiments were conducted in what has be...
Genetic Programming (GP) is an automatic programming methodology using mechanisms inspired by biolo...
Evolutionary algorithms have been gaining increased attention the past few years because of their ve...
Some recent work in the field of Genetic Programming (GP) has been concerned with finding optimum re...
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...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
Genetic algorithms are commonly used for automatically solving complex design problem because explor...
The thesis is about linear genetic programming (LGP), a machine learning approach that evolves compu...
Genetic programming (GP) can be viewed as the use of genetic algorithms (GAs) to evolve computationa...
[[abstract]]Although genetic programming (GP) is derived from genetic algorithm (GA), there are issu...
Many problems do not have a direct solution in the form of a known algorithm or program to solve suc...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
A parallel implementation of Genetic Programming using PVM is described. Two different topologies fo...
Introduction Genetic programming is a domain-independent problem-solving approach in which computer ...
It is approximately 50 years since the first computational experiments were conducted in what has be...
Genetic Programming (GP) is an automatic programming methodology using mechanisms inspired by biolo...
Evolutionary algorithms have been gaining increased attention the past few years because of their ve...
Some recent work in the field of Genetic Programming (GP) has been concerned with finding optimum re...
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...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
Genetic algorithms are commonly used for automatically solving complex design problem because explor...
The thesis is about linear genetic programming (LGP), a machine learning approach that evolves compu...
Genetic programming (GP) can be viewed as the use of genetic algorithms (GAs) to evolve computationa...
[[abstract]]Although genetic programming (GP) is derived from genetic algorithm (GA), there are issu...
Many problems do not have a direct solution in the form of a known algorithm or program to solve suc...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
A parallel implementation of Genetic Programming using PVM is described. Two different topologies fo...
Introduction Genetic programming is a domain-independent problem-solving approach in which computer ...
It is approximately 50 years since the first computational experiments were conducted in what has be...
Genetic Programming (GP) is an automatic programming methodology using mechanisms inspired by biolo...
Evolutionary algorithms have been gaining increased attention the past few years because of their ve...