Entropy-Driven Adaptive Representation

author = "Justinian P. Rosca",

title = "Entropy-Driven Adaptive Representation",

booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications",

year = "1995",

editor = "Justinian P. Rosca",

pages = "23--32",

address = "Tahoe City, California, USA",

month = "9 " # jul,

keywords = "genetic algorithms, genetic programming",

URL = "

ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.ml.gpw.ps.gz",

size = "10 pages",

abstract = "In the first genetic programming (GP) book John Koza noticed that fitness histograms give a highly informative global view of the evolutionary process [Koza, 1992]. The idea is further developed in this paper by discussing GP evolution in analogy to a physical system. I focus on three inter-related major goals: (1) Study the the problem of search effort allocation in GP; (2) Develop methods in the GA/GP framework that allow adaptive control of diversity; (3) Study ways of adaptation for faster convergence to optimal solution. An entropy measure based on phenotype classes is introduced which abstracts fitness histograms. In this context, entropy represents a measure of population diversity. An analysis of entropy plots and their correlation with other statistics from the population enables an intelligent adaptation of search control.",

notes = "part of \cite{rosca:1995:ml} free energy. Shannon, 1949. Chaitin 1987. Brief discussion of second law of thermodynamics in natural evolution of living systems.",