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GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
15 years 8 months ago
Multi-chromosomal genetic programming
This paper introduces an evolutionary algorithm which uses multiple chromosomes to evolve solutions to a symbolic regression problem. Inspiration for this algorithm is provided by...
Rachel Cavill, Stephen L. Smith, Andrew M. Tyrrell
109
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CISS
2008
IEEE
15 years 9 months ago
A genetic algorithm for designing constellations with low error floors
—The error floor of bit-interleaved coded modulation with iterative decoding (BICM-ID) can be minimized for a particular constellation by maximizing the harmonic mean of the squ...
Matthew C. Valenti, Raghu Doppalapudi, Don J. Torr...
BIOSYSTEMS
2007
115views more  BIOSYSTEMS 2007»
15 years 2 months ago
Evolving fuzzy rules to model gene expression
This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polis...
Ricardo Linden, Amit Bhaya
EC
2006
136views ECommerce» more  EC 2006»
15 years 2 months ago
Error Thresholds in Genetic Algorithms
The error threshold of replication is an important notion of the quasispecies evolution model; it is a critical mutation rate (error rate) beyond which structures obtained by an e...
Gabriela Ochoa
AAAI
1994
15 years 3 months ago
Evolving Neural Networks to Focus Minimax Search
Neural networks were evolved through genetic algorithms to focus minimax search in the game of Othello. At each level of the search tree, the focus networks decide which moves are...
David E. Moriarty, Risto Miikkulainen