- author = "M.-R. Akbarzadeh-T. and E. Tunstel and M. Jamshidi",
- title = "Genetic Algorithms and Genetic Programming: Combining Strength in One Evolutionary Strategy",
- booktitle = "Proceedings of the 1997 WERC/HSRC Joint Conference on the Environment",
- year = "1997",
- pages = "373--377",
- address = "Albuquerque, NM, USA",
- month = "26-29 " # apr,
-
organisation = "WERC Waste-management Education & Research Consortium
New Mexico State University Box 30001, Department WERC
Las Cruces, NM 88003-8001, USA
HSRC Great Plains/Rocky Mountain Hazardous Substance Research Center Kansas State University 101 Ward Hall Manhattan, KS 66506-2502, USA",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Akbarzadeh_1997_jce.pdf",
- size = "5 pages",
- abstract = "Genetic Algorithms (GA) and Genetic Programs (GP) are two of the most widely used evolution strategies for parameter optimisation 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 especially 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 optimising robot performance in manipulating hazardous waste. The first example involves optimisation 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.",
