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145
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GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
15 years 10 months ago
Genetic algorithms using low-discrepancy sequences
The random number generator is one of the important components of evolutionary algorithms (EAs). Therefore, when we try to solve function optimization problems using EAs, we must ...
Shuhei Kimura, Koki Matsumura
ATS
2000
IEEE
98views Hardware» more  ATS 2000»
15 years 9 months ago
Embedded core testing using genetic algorithms
Testing of embedded cores is very difficult in SOC (system-on-a-chip), since the core user may not know the gate level implementation of the core, and the controllability and obse...
Ruofan Xu, Michael S. Hsiao
142
Voted
ICCD
2000
IEEE
120views Hardware» more  ICCD 2000»
15 years 9 months ago
Equivalence Checking Combining a Structural SAT-Solver, BDDs, and Simulation
This paper presents a verification technique for functional comparison of large combinational circuits using a novel combination of known approaches. The idea is based on a tight...
Viresh Paruthi, Andreas Kuehlmann
ICDCS
1999
IEEE
15 years 9 months ago
Interpreting Stale Load Information
In this paper we examine the problem of balancing load in a large-scale distributed system when information about server loads may be stale. It is well known that sending each req...
Michael Dahlin
GECCO
2000
Springer
120views Optimization» more  GECCO 2000»
15 years 8 months ago
An Improved Adaptive Multi-Start Approach to Finding Near-Optimal Solutions to the Euclidean TSP
We present an "adaptive multi-start" genetic algorithm for the Euclidean traveling salesman problem that uses a population of tours locally optimized by the Lin-Kernigha...
Dan Bonachea, Eugene Ingerman, Joshua Levy, Scott ...