This paper provides a short, informal illustration of a selection scheme based on the key idea of competition, particularly suited for genetic programming, which provides a way to do without the explicit deenition of a tness function. In many tasks, competition between two individuals on one problem instance chosen according to some probability can be a valid alternative to deening an appropria...

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 common tree-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 ...

This paper presents a linear code referencing approach to the representation of individuals within a genetic programming scheme. This approach has been devised in order to confront various problems associated with genetic programming schemes. These are primarily the size of the available search space, the ability to pass through this search space, the construction of valid individuals after cro...

This paper explores the idea of using Genetic Programming (GP) to evolve Java Virtual Machine (JVM) byte code to solve a sample symbolic regression problem. The evolutionary process is done completely in memory using a standard Java environment.

Some recent work in the field of Genetic Programming (GP) has been concerned with finding optimum representations for evolvable and efficient computer programs. In this paper, I describe a new GP system in which target programs run on a stack-based virtual machine. The system is shown to have certain advantages in terms of efficiency and simplicity of implementation, and for certain classes of ...

Physical measurements are generally accompanied by their units of measurement. This contribution introduces an extension of genetic programming that exploits the information captured in the units of measurement and compares it against standard methods of genetic programming. The motivations for the development of this dimensionally-aware GP are twofold: to enhance the search efficiency by utili...

We consider the validation of randomly generated patterns in a Monte-Carlo Tree Search program. Our bandit-based genetic programming (BGP) algorithm, with proved mathematical properties, outperformed a highly optimized handcrafted module of a well-known computer-Go program with several world records in the game of Go.

The paper addresses a new implementation of genetic programming by using molecular approach. Our method is based on data¯ow techniques in DNA computing. After description of fundamental operations on DNA molecules and construction of logical functions the genetic programming method is introduced. We propose a way to handle these graph encoding molecules and which can be considered a genetic pro...

INTRODUCTION Evolutionary computation (EC) is the study of computational systems that borrow ideas from and are inspired by natural evolution and adaptation (Yao & Xu, 2006, pp. 1-18). EC covers a number of techniques based on evolutionary processes and natural selection: evolutionary strategies, genetic algorithms and genetic programming (Keedwell & Narayanan, 2005). Evolutionary strategies ar...

We show genetic programming (GP) populations can evolve under the influence of a Pareto multi-objective fitness and program size selection scheme, from “perfect” programs which match the training material to general solutions. The technique is demonstrated with programmatic image compression, two machine learning benchmark problems (Pima Diabetes and Wisconsin Breast Cancer) and an insurance cu...