%0 Conference Paper %1 kokai1998 %A Kókai, Gabriella %A Tóth, Zoltán %A Ványi, Robert %B Proceedings of the International Symposium on Engineering of Intelligent Systems, EIS'98 %D 1998 %E Alpaydin, E. %E Fyfe, Colin %K L-systems algorithms, genetic lindenmayer programming, %P 324--331 %T Application of Genetic Algorithms with more Populations for Lindenmayer Systems %U http://citeseer.ist.psu.edu/515449.html %X The paper describes a solution of the inverse problem for Lindenmayer systems with genetic programming. Inverse problem means that we try to evolve Lindenmayer grammars to describe fractal images. Genetic algorithm is used to evolve the rewriting rules of the system. As a starting point the solutions of Koza 7 and Jacob 6 is applied but these solutions were extended: for different types of fractal images, and we executed some modifications: more populations are processed parallel during the evolution process, where the selection of the individuals after the meeting of the populations can happen on the basis of either the best or the median fitness value or the user himself can select from the individuals. A further improvement we applied is an adaptation scheme for the application probability of genetic operators. With the help of this method, operators which produce better individuals can be used more frequently, so better individuals are more likely produced. Thus, with the help of them the speed of convergence can be significantly increased.

@inproceedings{kokai1998, abstract = {The paper describes a solution of the inverse problem for Lindenmayer systems with genetic programming. Inverse problem means that we try to evolve Lindenmayer grammars to describe fractal images. Genetic algorithm is used to evolve the rewriting rules of the system. As a starting point the solutions of Koza [7] and Jacob [6] is applied but these solutions were extended: for different types of fractal images, and we executed some modifications: more populations are processed parallel during the evolution process, where the selection of the individuals after the meeting of the populations can happen on the basis of either the best or the median fitness value or the user himself can select from the individuals. A further improvement we applied is an adaptation scheme for the application probability of genetic operators. With the help of this method, operators which produce better individuals can be used more frequently, so better individuals are more likely produced. Thus, with the help of them the speed of convergence can be significantly increased.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {K\'okai, Gabriella and T\'oth, Zolt\'an and V\'anyi, Robert}, biburl = {https://www.bibsonomy.org/bibtex/2f7a6f3194e967d4a3b62d86bb14bbc3c/brazovayeye}, booktitle = {Proceedings of the International Symposium on Engineering of Intelligent Systems, EIS'98}, editor = {Alpaydin, E. and Fyfe, Colin}, interhash = {5d1507740b40052c26a62255eaefedf1}, intrahash = {f7a6f3194e967d4a3b62d86bb14bbc3c}, keywords = {L-systems algorithms, genetic lindenmayer programming,}, pages = {324--331}, size = {7 pages}, timestamp = {2008-06-19T17:43:34.000+0200}, title = {Application of Genetic Algorithms with more Populations for Lindenmayer Systems}, url = {http://citeseer.ist.psu.edu/515449.html}, year = 1998 }