Fitness Distance Correlation in Structural Mutation Genetic Programming

A new kind of mutation for genetic programming based on the structural distance operators for trees is presented in this paper. We firstly describe a new genetic programming process based on these operators (we call it structural mutation genetic programming). Then we use structural distance to calculate the fitness distance correlation coefficient and we show that this coefficient is a reasonable measure to express problem difficulty for structural mutation genetic programming for the considered set of problems, i.e. unimodal trap functions, royal trees and MAX problem.

[1]  Philippe Collard,et al.  Fitness Distance Correlation, as statistical measure of Genetic Algorithm difficulty, revisited , 1998, ECAI.

[2]  Anikó Ekárt,et al.  Maintaining the Diversity of Genetic Programs , 2002, EuroGP.

[3]  N. Hopper,et al.  Analysis of genetic diversity through population history , 1999 .

[4]  Erik D. Goodman,et al.  The royal tree problem, a benchmark for single and multiple population genetic programming , 1996 .

[5]  Nikolay I. Nikolaev,et al.  Fitness Landscapes and Inductive Genetic Programming , 1997, ICANNGA.

[6]  Victor J. Rayward-Smith,et al.  Fitness Distance Correlation and Ridge Functions , 1998, PPSN.

[7]  Una-May O'Reilly,et al.  An analysis of genetic programming , 1995 .

[8]  P. Ross,et al.  An adverse interaction between crossover and restricted tree depth in genetic programming , 1996 .

[9]  Graham Kendall,et al.  Advanced Population Diversity Measures in Genetic Programming , 2002, PPSN.

[10]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[11]  M. Clergue,et al.  Misleading functions designed from alternation , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[12]  Leonardo Vanneschi,et al.  Fitness Distance Correlation And Problem Difficulty For Genetic Programming , 2002, GECCO.

[13]  Lee Altenberg,et al.  Fitness Distance Correlation Analysis: An Instructive Counterexample , 1997, ICGA.

[14]  Juan Julián Merelo Guervós,et al.  Parallel Problem Solving from Nature — PPSN VII , 2002, Lecture Notes in Computer Science.

[15]  Tim Jones.  Evolutionary Algorithms, Fitness Landscapes and Search , 1995 .

[16]  W. Langdon.  An Analysis of the MAX Problem in Genetic Programming , 1997 .