We show negative results about the automatic generation of programs within bounded-time. Combining recursion theory and statistics, we contrast these negative results with positive computability results for iterative approachs like genetic programming, provided that the fitness combines e.g. fastness and size. We then show that simulation-based approachs (approachs evaluating only by simulation the quality of programs) like GP are not too far from the minimal time required for evaluating these combined fitnesses
Abstract: Genetic programming is a machine learning technique to automatically create computer progr...
In this paper, we carry out experimental investigations that complement recent theoretical investiga...
Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand ...
We show negative results about the automatic generation of programs within bounded-time. Combining r...
International audienceInspired by genetic programming (GP), we study iterative algorithms for non-co...
In genetic programming (GP), controlling complexity often means reducing the size of evolved express...
We use the minimal instruction set F-4 computer to define a minimal Turing complete T7 computer suit...
Complexity of evolving models in genetic programming (GP) can impact both the quality of the models ...
The genetic algorithm (GA) is a machine-based optimization routine which connects evolutionary learn...
In the theory of evolutionary algorithms (EAs), computational time complexity is an essential proble...
Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated,...
International audienceThis paper proposes a theoretical analysis of Genetic Programming (GP) from th...
: Genetic Programming is a method for evolving functions that find approximate or exact solutions to...
Traditionally, reducing complexity in Machine Learning promises benefits such as less overfitting. H...
The quest for simple solutions is not new in machine learning (ML) and related methods such as genet...
Abstract: Genetic programming is a machine learning technique to automatically create computer progr...
In this paper, we carry out experimental investigations that complement recent theoretical investiga...
Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand ...
We show negative results about the automatic generation of programs within bounded-time. Combining r...
International audienceInspired by genetic programming (GP), we study iterative algorithms for non-co...
In genetic programming (GP), controlling complexity often means reducing the size of evolved express...
We use the minimal instruction set F-4 computer to define a minimal Turing complete T7 computer suit...
Complexity of evolving models in genetic programming (GP) can impact both the quality of the models ...
The genetic algorithm (GA) is a machine-based optimization routine which connects evolutionary learn...
In the theory of evolutionary algorithms (EAs), computational time complexity is an essential proble...
Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated,...
International audienceThis paper proposes a theoretical analysis of Genetic Programming (GP) from th...
: Genetic Programming is a method for evolving functions that find approximate or exact solutions to...
Traditionally, reducing complexity in Machine Learning promises benefits such as less overfitting. H...
The quest for simple solutions is not new in machine learning (ML) and related methods such as genet...
Abstract: Genetic programming is a machine learning technique to automatically create computer progr...
In this paper, we carry out experimental investigations that complement recent theoretical investiga...
Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand ...