Visual analysis of evolutionary algorithms

The non-linear complexity of evolutionary algorithms (EAs) make them a challenge to understand. The difficulty in performing detailed analyses of an EA is in sorting through the large amount of of data that can be generated in a single run. This paper describes a visualization tool that facilitates navigation through the details of an EA run. The visualization tool organizes and displays EA data at various levels of detail and allows for easy transitions between related pieces of data.

[1]  Annie S. Wu,et al.  Putting More Genetics into Genetic Algorithms , 1998, Evolutionary Computation.

[2]  Annie S. Wu,et al.  Genome Length as an Evolutionary Self-adaptation , 1998, PPSN.

[3]  W. B. Shine,et al.  Visualizing the evolution of genetic algorithm search processes , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[4]  Annie S. Wu,et al.  Empirical Observations on the Roles of Crossover and Mutation , 1997, ICGA.

[5]  Trevor D. Collins Using Software Visualisation Technology to Help Evolutionary Algorithm Users Validate Their Solutions , 1997, ICGA.

[6]  Annie S. Wu,et al.  A Comparison of the Fixed and Floating Building Block Representation in the Genetic Algorithm , 1996, Evolutionary Computation.

[7]  Stephanie Forrest,et al.  An Introduction to SFI Echo , 1993 .

[8]  Edward Rolf Tufte,et al.  The visual display of quantitative information , 1985 .

[9]  Annie S. Wu,et al.  Empirical Studies of the Genetic Algorithm with Noncoding Segments , 1995, Evolutionary Computation.

[10]  Melanie Mitchell,et al.  Relative Building-Block Fitness and the Building Block Hypothesis , 1992, FOGA.

[11]  Trevor Collins,et al.  Understanding evolutionary computing: a hands on approach , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[12]  P. Fayers,et al.  The Visual Display of Quantitative Information , 1990 .