The thesis is about linear genetic programming (LGP), a machine learning approach that evolves computer programs as sequences of imperative instructions. Two fundamental differences to the more commontree-based variant (TGP) may be identified. These are the graph-based functional structure of linear genetic programs, on the one hand, and the existence of structurally noneffective code, on the other hand.The two major objectives of this work comprise(1) the development of more advanced methods and variation operators to produce better and more compact program solutions and (2) the analysis of general EA/GP phenomena in linear GP, including intron code, neutral variations, and code growth, among others.First, we introduce efficient algorithms...
is a recently introduced form of Genetic Programming (GP), rooted in a geometric theory of represent...
Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has...
Genetic programming (GP) has been successfully applied to solving multiclass classification problems...
Genetic Programming (“GP”) is a machine learning algorithm. Typically, Genetic Programming is a supe...
Different variants of genetic operators are introduced and compared for linear genetic programming i...
In recent years different genetic programming (GP) structures have emerged. Today, the basic forms ...
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...
Abstract: Genetic programming (GP) is an automated method for creating a working computer program ...
A novel method of using Machine Learning (ML) algorithms to improve the performance of Linear Geneti...
Genetic Programming is an evolutionary computation technique which searches for those computer progr...
Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has...
We investigate structural and semantic distance metrics for linear genetic programs. Causal connecti...
Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms...
Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated,...
is a recently introduced form of Genetic Programming (GP), rooted in a geometric theory of represent...
Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has...
Genetic programming (GP) has been successfully applied to solving multiclass classification problems...
Genetic Programming (“GP”) is a machine learning algorithm. Typically, Genetic Programming is a supe...
Different variants of genetic operators are introduced and compared for linear genetic programming i...
In recent years different genetic programming (GP) structures have emerged. Today, the basic forms ...
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...
Abstract: Genetic programming (GP) is an automated method for creating a working computer program ...
A novel method of using Machine Learning (ML) algorithms to improve the performance of Linear Geneti...
Genetic Programming is an evolutionary computation technique which searches for those computer progr...
Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has...
We investigate structural and semantic distance metrics for linear genetic programs. Causal connecti...
Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms...
Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated,...
is a recently introduced form of Genetic Programming (GP), rooted in a geometric theory of represent...
Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has...
Genetic programming (GP) has been successfully applied to solving multiclass classification problems...