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PLDI
2003
ACM
14 years 22 days ago
Meta optimization: improving compiler heuristics with machine learning
Compiler writers have crafted many heuristics over the years to approximately solve NP-hard problems efficiently. Finding a heuristic that performs well on a broad range of applic...
Mark Stephenson, Saman P. Amarasinghe, Martin C. M...
EH
1999
IEEE
351views Hardware» more  EH 1999»
13 years 11 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...
EVOW
2007
Springer
13 years 11 months ago
Evaluation of Different Metaheuristics Solving the RND Problem
RND (Radio Network Design) is a Telecommunication problem consisting in covering a certain geographical area by using the smallest number of radio antennas achieving the biggest co...
Miguel A. Vega-Rodríguez, Juan Antonio G&oa...
GECCO
2006
Springer
170views Optimization» more  GECCO 2006»
13 years 11 months ago
How an optimal observer can collapse the search space
Many metaheuristics have difficulty exploring their search space comprehensively. Exploration time and efficiency are highly dependent on the size and the ruggedness of the search...
Christophe Philemotte, Hugues Bersini
GECCO
2008
Springer
183views Optimization» more  GECCO 2008»
13 years 8 months ago
UMDAs for dynamic optimization problems
This paper investigates how the Univariate Marginal Distribution Algorithm (UMDA) behaves in non-stationary environments when engaging in sampling and selection strategies designe...
Carlos M. Fernandes, Cláudio F. Lima, Agost...