- author = "Anna L. Tyler and Bill C. White and Casey S. Greene and Peter C. Andrews and Richard Cowper-Sal-lari and Jason H. Moore",
- title = "Development and Evaluation of an Open-Ended Computational Evolution System for the Creation of Digital Organisms with Complex Genetic Architecture",
- booktitle = "2009 IEEE Congress on Evolutionary Computation",
- year = "2009",
- editor = "Andy Tyrrell",
- pages = "2907--2912",
- address = "Trondheim, Norway",
- month = "18-21 " # may,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- isbn13 = "978-1-4244-2959-2",
- file = "P105.pdf",
-
DOI = "
doi:10.1109/CEC.2009.4983308",
- abstract = "Epistasis, or gene-gene interaction, is a ubiquitous phenomenon that is inadequately addressed in human genetic studies. There are few tools that can accurately identify high order epistatic interactions, and there is a lack of general understanding as to how epistatic interactions fit into genetic architecture. Here we approach both problems through the lens of genetic programming (GP). It has recently been proposed that increasing open-endedness of GP will result in more complex solutions that better acknowledge the complexity of human genetic datasets. Moreover, the solutions evolved in open-ended GP can serve as model organisms in which to study general effects of epistasis on phenotype. Here we introduce a prototype computational evolution system that implements an open-ended GP and generates organisms that display epistatic interactions. These interactions are significantly more prevalent and have a greater effect on fitness than epistatic interactions in organisms generated in the absence of selection.",
- keywords = "genetic algorithms, genetic programming",
-
notes = "epistasis in open-ended GP by replacing two
instructions (genes) with NOP.
Fig 2. shows first instruction liable to have epistasis with many other instructions in (linear) organism.
Nested toroidal grids (8 neighbours) of populations: mutation probability (1x1), mutation operators (3x3), solution operations (9x9), Avida like code (18x18). Regression 2x+4. C++. Fitness of each operator given by averaging over 3x3 organisms it has evolved. 100 gens (5 secs) bloats average 8-> 20 instructions per organism.
P2911 {"}Alife and GP poised to help us take great strides in our understanding of human genetic and the genetic of human common disease. cites \cite{DusanMisevic02222006}.
CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR",
