%0 Conference Paper %1 Akbarzadeh:1997:jce %A Akbarzadeh-T., M.-R. %A Tunstel, E. %A Jamshidi, M. %B Proceedings of the 1997 WERC/HSRC Joint Conference on the Environment %C Albuquerque, NM, USA %D 1997 %K algorithms, genetic programming %P 373--377 %T Genetic Algorithms and Genetic Programming: Combining Strength in One Evolutionary Strategy %X Genetic Algorithms (GA) and Genetic Programs (GP) are two of the most widely used evolution strategies for parameter optimization of complex systems. GAs have shown a great deal of success where the representation space is a string of binary or real-valued numbers. At the same time, GP has demonstrated success with symbolic representation spaces and where structure among symbols is explored. This paper discusses weaknesses and strengths of GA and GP in search of a combined and more evolved optimization algorithm. This combination is espeially attractive for problem domains with non-homogeneous parameters. In particular, a fuzzy logic membership function is represented by numerical strings, whereas rule-sets are represented by symbols and structural connectives. Two examples are provided which exhibit how GA and GP are best used in optimizing robot performance in manipulating hazardous waste. The first example involves optimization for a fuzzy controller for a flexible robot using GA and the second example illustrates usage of GP in optimizing an intelligent navigation algorithm for a mobile robot. A novel strategy for combining GA and GP is presented.

@inproceedings{Akbarzadeh:1997:jce, abstract = {Genetic Algorithms (GA) and Genetic Programs (GP) are two of the most widely used evolution strategies for parameter optimization of complex systems. GAs have shown a great deal of success where the representation space is a string of binary or real-valued numbers. At the same time, GP has demonstrated success with symbolic representation spaces and where structure among symbols is explored. This paper discusses weaknesses and strengths of GA and GP in search of a combined and more evolved optimization algorithm. This combination is espeially attractive for problem domains with non-homogeneous parameters. In particular, a fuzzy logic membership function is represented by numerical strings, whereas rule-sets are represented by symbols and structural connectives. Two examples are provided which exhibit how GA and GP are best used in optimizing robot performance in manipulating hazardous waste. The first example involves optimization for a fuzzy controller for a flexible robot using GA and the second example illustrates usage of GP in optimizing an intelligent navigation algorithm for a mobile robot. A novel strategy for combining GA and GP is presented.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Albuquerque, NM, USA}, author = {Akbarzadeh-T., M.-R. and Tunstel, E. and Jamshidi, M.}, biburl = {https://www.bibsonomy.org/bibtex/2a8dfc6bfb19bc3819883fa9176e71647/brazovayeye}, booktitle = {Proceedings of the 1997 WERC/HSRC Joint Conference on the Environment}, interhash = {396a35cefec3e87462816d576a99f5b1}, intrahash = {a8dfc6bfb19bc3819883fa9176e71647}, keywords = {algorithms, genetic programming}, month = {April}, pages = {373--377}, timestamp = {2008-06-19T17:35:22.000+0200}, title = {Genetic Algorithms and Genetic Programming: Combining Strength in One Evolutionary Strategy}, year = 1997 }