- author = "Stuart William Card",
- title = "Towards an Information Theoretic Framework for Evolutionary Learning",
- school = "Electrical Engineering and Computer Science, Syracuse University",
- year = "2011",
- address = "USA",
- month = aug,
- email = "cards@ntcnet.com",
- keywords = "genetic algorithms, genetic programming, diversity, ensemble model, evolvability, fitness, information distance, mutual information",
-
URL = "
https://surface.syr.edu/eecs_etd/307",
-
URL = "https://surface.syr.edu/cgi/viewcontent.cgi?article=1311&context=eecs_etd",
- size = "219 pages",
-
abstract = "The vital essence of evolutionary learning consists of
information flows between the environment and the
entities differentially surviving and reproducing
therein. Gain or loss of information in individuals and
populations due to evolutionary steps should be
considered in evolutionary algorithm theory and
practice. Information theory has rarely been applied to
evolutionary computation, a lacuna that this
dissertation addresses, with an emphasis on objectively
and explicitly evaluating the ensemble models implicit
in evolutionary learning. Information theoretic
functionals can provide objective, justifiable,
general, computable, commensurate measures of fitness
and diversity.
We identify information transmission channels implicit in evolutionary learning. We define information distance metrics and indices for ensembles. We extend Price's Theorem to non-random mating, give it an effective fitness interpretation and decompose it to show the key factors influencing heritability and evolvability. We argue that heritability and evolvability of our information theoretic indicators are high. We illustrate use of our indices for reproductive and survival selection. We develop algorithms to estimate information theoretic quantities on mixed continuous and discrete data via the empirical copula and information dimension. We extend statistical resampling. We present experimental and real world application results: chaotic time series prediction; parity; complex continuous functions; industrial process control; and small sample social science data. We formalize conjectures regarding evolutionary learning and information geometry.",
-
notes = "Information Theoretic Evaluations of Ensembles.
Corollaries to Price's Theorem
Supervisor: Chilukuri K. Mohan",
