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Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics

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Genetic Programming Theory and Practice VII

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

The relationship between interindividual variation in our genomes and variation in our susceptibility to common diseases is expected to be complex with multiple interacting genetic factors. A central goal of human genetics is to identify which DNA sequence variations predict disease risk in human populations. Our success in this endeavour will depend critically on the development and implementation of computational intelligence methods that are able to embrace, rather than ignore, the complexity of the genotype to phenotype relationship. To this end, we have developed a computational evolution system (CES) to discover genetic models of disease susceptibility involving complex relationships between DNA sequence variations. The CES approach is hierarchically organized and is capable of evolving operators of any arbitrary complexity. The ability to evolve operators distinguishes this approach from artificial evolution approaches using fixed operators such as mutation and recombination. Our previous studies have shown that a CES that can utilize expert knowledge about the problem in evolved operators significantly outperforms a CES unable to use this knowledge. This environmental sensing of external sources of biological or statistical knowledge is important when the search space is both rugged and large as in the genetic analysis of complex diseases. We show here that the CES is also capable of evolving operators which exploit one of several sources of expert knowledge to solve the problem. This is important for both the discovery of highly fit genetic models and because the particular source of expert knowledge used by evolved operators may provide additional information about the problem itself. This study brings us a step closer to a CES that can solve complex problems in human genetics in addition to discovering genetic models of disease.

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Authors and Affiliations

  1. Dartmouth College, One Medical Center Drive, HB7937, Lebanon, NH, 03756, USA

    Casey S. Greene, Douglas P. Hill & Jason H. Moore

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  1. Casey S. Greene
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  2. Douglas P. Hill
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  3. Jason H. Moore
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Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, U.S.A.

    Rick Riolo

  2. Computer Science &, Massachusetts Institute of Technology, Vassar St. 32, Cambridge, 02139, U.S.A.

    Una-May O'Reilly

  3. Solido Design Automation, Inc., Research Drive 102-116, Saskatoon, S7N 3R3, Canada

    Trent McConaghy

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Greene, C.S., Hill, D.P., Moore, J.H. (2010). Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics. In: Riolo, R., O'Reilly, UM., McConaghy, T. (eds) Genetic Programming Theory and Practice VII. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1626-6_2

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  • DOI: https://doi.org/10.1007/978-1-4419-1626-6_2

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  • Publisher Name: Springer, Boston, MA

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