Types have been introduced to Genetic Programming (GP) by researchers with different motivation. We present the concept of types in GP and introduce a typed GP system, PolyGP, that supports polymorphism through the use of three different kinds of type variable. We demonstrate the usefulness of this kind of polymorphism in GP by evolving two polymorphic programs (nth and map) using the system. B...

Robocode is a virtual environment in which in Java programmed bots compete. It is a perfect environment to apply and test Genetic Programming. This thesis describes an implementation which uses both grammar and trees to generate and mutate Java code. Populations, which evolve parallel, focus on different aspects of Robocode bots to refrain from converging to local maxima. The are mixed at the e...

The heritability of hypertension (HTN) is widely recognized and as a result, extensive studies ranging from genetic linkage analyses to genome-wide association studies are actively ongoing to elucidate the etiology of both monogenic and polygenic forms of HTN. Due to the complex nature of essential HTN, however, single genes affecting blood pressure (BP) variability remain difficult to isolate ...

This paper describes Parallel Distributed Genetic Programming (PDGP), a new form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of paral-lelism and an eecient and eeective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the ow of control and results. In ...

The genetic programming (GP) paradigm is a functional approach to inductively forming programs. The use of natural selection based on a tness function for reproduction of the program population has allowed many problems to be solved that require a non-xed representation. Attempts to extend GP have focussed on typing the language to restrict crossover and to ensure legal programs are always crea...

Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection (Koza, 1992). However, in its standard form, there is no way to restrict the programs it generates to those where the functions operate on appropriate data types. In the case when the programs manipulate multiple data types and contain functions designed to operate on par...

This paper presents an approach for designing classifiers for a multiclass problem using Genetic Programming (GP). GP can discover relationships among observed data and express them mathematically. The proposed approach takes an integrated view of all classes when GP evolves. For a multiclass problem, an individual of the population will contain tree for every class. The GP is trained with a se...

Genetic Programming in Hardware This thesis describes a hardware implementation of a complete Genetic Programming (GP) system using a Field Programmable Gate Array, which is shown to speed-up GP by over 400 times when compared with a software implementation of the same algorithm. The hardware implements the creation of the initial population, breeding operators, parallel fitness evaluations and...

The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called “machine intelligence” (Turing, 1948, 1950). In his talk entitled AI: Where It Has Been and Where It Is Going, machine learning pioneer Arthur Samuel stated the main goal of the fields of machine learning and artificial intel...