Title :
Applying logic grammars to induce sub-functions in genetic programming
Author :
Wong, Man Leung ; Leung, Kwong Sak
Author_Institution :
Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Shatin, Hong Kong
fDate :
29 Nov-1 Dec 1995
Abstract :
Genetic programming (GP) is a method of automatically inducing S-expressions in LISP to perform specified tasks. The problem of inducing programs can be reformulated as a search for a highly fit program in the space of all possible programs. This paper presents a framework in which the search space can be specified declaratively by a user. Its application in inducing sub-functions is detailed. The framework is based on a formalism of logic grammars and it is implemented as a system called LOGENPRO (LOgic grammar-based GENetic PROgramming system). The formalism is powerful enough to represent context-sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the programs induced. The system is also very flexible and programs in various programming languages can be acquired. Automatic discovery of sub-functions is one of the most important research areas in GP. An experiment is used to demonstrate that LOGENPRO can emulate Koza´s (1992, 1994) automatically defined functions (ADF). Moreover, LOGENPRO can employ knowledge such as argument types in a unified framework. An experiment shows that LOGENPRO has a superior performance to that of ADF when more domain-dependent knowledge is available
Keywords :
LISP; automatic programming; context-sensitive grammars; functions; genetic algorithms; learning (artificial intelligence); programming theory; software performance evaluation; subroutines; LISP; LOGENPRO; S-expressions; argument types; automatic program discovery; automatically defined functions; context-sensitive information; declaratively specified search space; domain-dependent knowledge; genetic programming; learning speed; logic grammars; performance evaluation; program fitness; program quality; programming languages; sub-function induction; Acceleration; Automatic logic units; Computer languages; Computer science; Genetic engineering; Genetic programming; Learning systems; Logic programming; Research and development management; Systems engineering and theory;
Conference_Titel :
Evolutionary Computation, 1995., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2759-4
DOI :
10.1109/ICEC.1995.487477