Fitness landscapes and difficulty in genetic programming

The structure of the fitness landscape on which genetic programming operates is examined. The landscapes of a range of problems of known difficulty are analyzed in an attempt to determine which landscape measures correlate with the difficulty of the problem. The autocorrelation of the fitness values of random walks, a measure which has been shown to be related to perceived difficulty using other techniques, is only a weak indicator of the difficulty as perceived by genetic programming. All of these problems show unusually low autocorrelation. Comparison of the range of landscape basin depths at the end of adaptive walks on the landscapes shows good correlation with problem difficulty, over the entire range of problems examined.<<ETX>>

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Kenneth A. De Jong,et al.  On Using Genetic Algorithms to Search Program Spaces , 1987, ICGA.

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[5]  Weinberger,et al.  Local properties of Kauffman's N-k model: A tunably rugged energy landscape. , 1991, Physical review. A, Atomic, molecular, and optical physics.

[6]  Marc Lipsitch,et al.  Adaptation on Rugged Landscapes Generated by Iterated Local Interactions of Neighboring Genes , 1991, ICGA.

[7]  Bernard Manderick,et al.  The Genetic Algorithm and the Structure of the Fitness Landscape , 1991, ICGA.

[8]  John R. Koza,et al.  Genetic Programming II , 1992 .

[9]  Peter F. Stadler,et al.  Correlation in Landscapes of Combinatorial Optimization Problems , 1992 .

[10]  P. Stadler,et al.  The landscape of the traveling salesman problem , 1992 .

[11]  John R. Koza.  Hierarchical Automatic Function Definition in Genetic Programming , 1992, FOGA.

[12]  Correlation structure of the landscape of the graph-bipartitioning problem , 1992 .

[13]  Kenneth E. Kinnear,et al.  Generality and Difficulty in Genetic Programming: Evolving a Sort , 1993, ICGA.

[14]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[15]  P. Schuster,et al.  Statistics of RNA secondary structures , 1993, Biopolymers.

[16]  K. E. Kinnear,et al.  Evolving a sort: lessons in genetic programming , 1993, IEEE International Conference on Neural Networks.

[17]  Weinberger,et al.  RNA folding and combinatory landscapes. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[18]  L. Altenberg.  The evolution of evolvability in genetic programming , 1994 .

[19]  J. K. Kinnear,et al.  Alternatives in automatic function definition: a comparison of performance , 1994 .