%0 Thesis %1 ekart:thesis %A Ekart, Aniko %D 2001 %K algorithms, genetic programming %T Genetic programming: new performance improving methods and applications %X Genetic programming is the newest form of evolutionary computation that was conceived in the late 1980's as a possible means for automatic programming. Genetic programming performs an evolutionary search in the space of computer programs and selects the program that solves a given task according to certain criteria. In the first part of the dissertation we give an overview of evolutionary computation and in particular genetic programming. We raise key issues for genetic programming: code growth, diversity, real world applications. In the second part we present our contribution to the theory of genetic programming. We demonstrate two methods for limiting the code growth. The first method consists in applying an additional mutation operator that simplifies the structure of a genetic program without altering its behavior. The second method applies multiobjective optimization for the objectives of fitness and program size. We show that both methods are successful in reducing code growth without significant loss of accuracy. We then define a distance metric for genetic programs and use it for applying the fitness sharing technique. We propose a simple diversity measure based on our metric and study the effects of fitness sharing with the help of this diversity measure. In the third part we show the application of genetic programming in two complex real world problems. The first problem comes from mechanical engineering. Four bar mechanisms play a very important role in practical mechanism design. We describe our four bar mechanism design system. We demonstrate how genetic programming can be a vital component of a complex design system. We integrate genetic programming with decision trees into a powerful learning machine. The second problem belongs to the decision support domain of economics. The decision-makers have to make many subjective decisions. Consequently, the final decision is sensitive to even small changes in these subjective values. We present our genetic programming system that helps the decision-makers to arrive at stable decisions. That is, for small variations in the values of the involved variables, the final decision remains unchanged.

@phdthesis{ekart:thesis, abstract = {Genetic programming is the newest form of evolutionary computation that was conceived in the late 1980's as a possible means for automatic programming. Genetic programming performs an evolutionary search in the space of computer programs and selects the program that solves a given task according to certain criteria. In the first part of the dissertation we give an overview of evolutionary computation and in particular genetic programming. We raise key issues for genetic programming: code growth, diversity, real world applications. In the second part we present our contribution to the theory of genetic programming. We demonstrate two methods for limiting the code growth. The first method consists in applying an additional mutation operator that simplifies the structure of a genetic program without altering its behavior. The second method applies multiobjective optimization for the objectives of fitness and program size. We show that both methods are successful in reducing code growth without significant loss of accuracy. We then define a distance metric for genetic programs and use it for applying the fitness sharing technique. We propose a simple diversity measure based on our metric and study the effects of fitness sharing with the help of this diversity measure. In the third part we show the application of genetic programming in two complex real world problems. The first problem comes from mechanical engineering. Four bar mechanisms play a very important role in practical mechanism design. We describe our four bar mechanism design system. We demonstrate how genetic programming can be a vital component of a complex design system. We integrate genetic programming with decision trees into a powerful learning machine. The second problem belongs to the decision support domain of economics. The decision-makers have to make many subjective decisions. Consequently, the final decision is sensitive to even small changes in these subjective values. We present our genetic programming system that helps the decision-makers to arrive at stable decisions. That is, for small variations in the values of the involved variables, the final decision remains unchanged.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Ekart, Aniko}, biburl = {https://www.bibsonomy.org/bibtex/2530e1bf3ca367a085822c6ef31bd0b41/brazovayeye}, email = {ekart@sztaki.hu}, interhash = {89df03171f9babe2a3910858369ac4bb}, intrahash = {530e1bf3ca367a085822c6ef31bd0b41}, keywords = {algorithms, genetic programming}, school = {E{\"{o}}tv{\"{o}}s Lorand University}, timestamp = {2008-06-19T17:39:11.000+0200}, title = {Genetic programming: new performance improving methods and applications}, year = 2001 }