TY - GEN
T1 - Conquering the needle-in-a-haystack
T2 - 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009
AU - Turner, Stephen D.
AU - Ritchie, Marylyn D.
AU - Bush, William S.
PY - 2009
Y1 - 2009
N2 - Evolutionary algorithms such as genetic programming and grammatical evolution have been used for simultaneously optimizing network architecture, variable selection, and weights for artificial neural networks. Using an evolutionary algorithm to perform variable selection while searching for non-linear interactions is akin to searching for a needle in a haystack. There is, however, a considerable amount of correlation among variables in biological datasets, such as in microarray or genetic studies. Using the XOR problem, we show that correlation between non-functional and functional variables alters the variable selection fitness landscape by broadening the fitness peak over a wider range of potential input variables. Furthermore, when sub-optimal weights are used, local optima in the variable selection fitness landscape appear centered on each of the two functional variables. These attributes of the fitness landscape may supply building blocks for evolutionary search procedures, and may provide a rationale for conducting a local search for variable selection.
AB - Evolutionary algorithms such as genetic programming and grammatical evolution have been used for simultaneously optimizing network architecture, variable selection, and weights for artificial neural networks. Using an evolutionary algorithm to perform variable selection while searching for non-linear interactions is akin to searching for a needle in a haystack. There is, however, a considerable amount of correlation among variables in biological datasets, such as in microarray or genetic studies. Using the XOR problem, we show that correlation between non-functional and functional variables alters the variable selection fitness landscape by broadening the fitness peak over a wider range of potential input variables. Furthermore, when sub-optimal weights are used, local optima in the variable selection fitness landscape appear centered on each of the two functional variables. These attributes of the fitness landscape may supply building blocks for evolutionary search procedures, and may provide a rationale for conducting a local search for variable selection.
UR - http://www.scopus.com/inward/record.url?scp=67650660993&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-01184-9_8
DO - 10.1007/978-3-642-01184-9_8
M3 - Conference contribution
AN - SCOPUS:67650660993
SN - 3642011837
SN - 9783642011832
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 80
EP - 91
BT - Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 7th European Conference, EvoBIO 2009, Proceedings
Y2 - 15 April 2009 through 17 April 2009
ER -