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» Unsupervised Problem Decomposition Using Genetic Programming
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EH
1999
IEEE
351views Hardware» more  EH 1999»
14 years 3 months ago
Evolvable Hardware or Learning Hardware? Induction of State Machines from Temporal Logic Constraints
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...
Marek A. Perkowski, Alan Mishchenko, Anatoli N. Ch...
IOR
2010
86views more  IOR 2010»
13 years 9 months ago
Disjunctive Decomposition for Two-Stage Stochastic Mixed-Binary Programs with Random Recourse
This paper introduces disjunctive decomposition for two-stage mixed 0-1 stochastic integer programs (SIPs) with random recourse. Disjunctive decomposition allows for cutting plane...
Lewis Ntaimo
CEC
2009
IEEE
14 years 5 months ago
Semantically driven mutation in genetic programming
—Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic changes i...
Lawrence Beadle, Colin G. Johnson
EH
2004
IEEE
107views Hardware» more  EH 2004»
14 years 2 months ago
Evolving Digital Circuits using Multi Expression Programming
Multi Expression Programming (MEP) is a Genetic Programming (GP) variant that uses linear chromosomes for solution encoding. A unique MEP feature is its ability of encoding multipl...
Mihai Oltean, Crina Grosan
ECML
2005
Springer
14 years 25 days ago
Clustering and Metaclustering with Nonnegative Matrix Decompositions
Although very widely used in unsupervised data mining, most clustering methods are affected by the instability of the resulting clusters w.r.t. the initialization of the algorithm ...
Liviu Badea