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We explore and attempt to understand both natural and simulated evolution. Our goal is to better explain what we observe in nature, to understand the limitations of evolutionary processes, and to apply simulated evolution to solve practical problems.
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We are applying evolutionary computation to address problems in bioinformatics; For example, we are developing and testing algorithms to use genetic programming and genetic algorithms for multiple (DNA or protein) sequence alignment, and for phylogenetic reconstruction. We have recently been exploring the limitations of progressive multiple sequence alignment.
We are developing techniques which enable us to infer the makeup of a microbial community from the DNA present in a sample, without our having to sample the DNA or culture the microbes. We are particularly interested in microbial communities in the human microbiome. See our Microbial Community Analysis (MiCA) website.
We have developed a sophisticated computer simulation system. This system simulates the evolution of DNA sequences which undergo reverse transcription. This simulator maintains complete sequence information and can be configured to accommodate a wide range of assumptions about the mechanisms and parameters involved. Our intended model system was mammalian retrotransposons, but the system is not tied to any particular biological system.
We have been investigating the phenomenon of code growth in genetic programming, from both a theoretical and an empirical point of view.
We have also investigated the effect of different pseudorandom number generators on the performance of genetic algorithms.
We are also exploring computational complexity and evolutionary computation.
27 April, 2008 21:04
