Visualizing the loss of diversity in genetic programming

This work introduces visualization techniques that allow for a multivariate approach in understanding the dynamics that underlie genetic programming (GP). Emphasis is given toward understanding the relationship between problem difficulty and the loss of diversity. The visualizations raise questions about diversity and problem solving efficacy, as well as the role of the initial population in determining solution outcomes.

[1]  Dana H. Ballard,et al.  Rooted-tree schemata in genetic programming , 1999 .

[2]  Jason M. Daida,et al.  Identifying Structural Mechanisms in Standard Genetic Programming , 2003, GECCO.

[3]  Jason M. Daida,et al.  Analysis of single-node (building) blocks in genetic programming , 1999 .

[4]  D. Ellis.  Visual explanations: Images and quantities , 1997 .

[5]  Edward R. Tufte,et al.  Envisioning Information , 1990 .

[6]  Byoung-Tak Zhang,et al.  Effects of selection schemes in genetic programming for time series prediction , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[7]  Jason M. Daida,et al.  Visualizing Tree Structures in Genetic Programming , 2003, GECCO.

[8]  Myke Gluck,et al.  Visual Explanations: Images and Quantities, Evidence and Narrative , 1997, Inf. Process. Manag..

[9]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.

[10]  B. Marx.  The Visual Display of Quantitative Information , 1985 .

[11]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

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

[13]  Lothar Thiele,et al.  A Mathematical Analysis of Tournament Selection , 1995, ICGA.

[14]  Jason M. Daida,et al.  What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming , 1999, Genetic Programming and Evolvable Machines.

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

[16]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[17]  Sean Luke,et al.  Population Implosion in Genetic Programming , 2003, GECCO.

[18]  Graham Kendall,et al.  A Survey And Analysis Of Diversity Measures In Genetic Programming , 2002, GECCO.

[19]  Tatsuya Motoki,et al.  Calculating the Expected Loss of Diversity of Selection Schemes , 2002, Evolutionary Computation.

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

[21]  M Pincus,et al.  An evolutionary strategy. , 1970, Journal of theoretical biology.

[22]  R. Galar,et al.  Handicapped individua in evolutionary processes , 1985, Biological Cybernetics.