We model the distribution of functions implemented by non-recursive programs, similar to linear genetic programming (GP). Most functions are constants, the remainder are mostly parsimonious. The effect of ad-hoc rules on GP are described and new heuristics are proposed. Bounds on how long programs need to be before the distribution of their functionality is close to its limiting distribution are provided in general and for average computers. Results for average computers and a model like genetic programming are experimentally tested
Abstract. This paper proposes a theoretical analysis of Genetic Pro-gramming (GP) from the perspecti...
The thesis is about linear genetic programming (LGP), a machine learning approach that evolves compu...
Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand ...
We model in detail the distribution of Boolean functions implemented by random non-recursive program...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
When using genetic programming (GP) or other techniques that try to approximate unknown functions, t...
When using genetic programming (GP) or other techniques that try to approximate unknown functions, t...
In this paper, we carry out experimental investigations that complement recent theoretical investiga...
Conventional genetic programming research excludes memory and iteration. We have begun an extensive ...
It is difficult to predict a genetic algorithm's behavior on an arbitrary problem. Combining ge...
We focus on the halting probability and the number of instructions executed by programs that halt fo...
We present N-gram GP, an estimation of distribution algorithm for the evolution of linear computer p...
International audienceThis paper proposes a theoretical analysis of Genetic Programming (GP) from th...
Genetic Programming (GP) automatically generates computer programs to solve specified problems. It d...
Abstract. This paper proposes a theoretical analysis of Genetic Pro-gramming (GP) from the perspecti...
The thesis is about linear genetic programming (LGP), a machine learning approach that evolves compu...
Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand ...
We model in detail the distribution of Boolean functions implemented by random non-recursive program...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
When using genetic programming (GP) or other techniques that try to approximate unknown functions, t...
When using genetic programming (GP) or other techniques that try to approximate unknown functions, t...
In this paper, we carry out experimental investigations that complement recent theoretical investiga...
Conventional genetic programming research excludes memory and iteration. We have begun an extensive ...
It is difficult to predict a genetic algorithm's behavior on an arbitrary problem. Combining ge...
We focus on the halting probability and the number of instructions executed by programs that halt fo...
We present N-gram GP, an estimation of distribution algorithm for the evolution of linear computer p...
International audienceThis paper proposes a theoretical analysis of Genetic Programming (GP) from th...
Genetic Programming (GP) automatically generates computer programs to solve specified problems. It d...
Abstract. This paper proposes a theoretical analysis of Genetic Pro-gramming (GP) from the perspecti...
The thesis is about linear genetic programming (LGP), a machine learning approach that evolves compu...
Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand ...