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GECCO
2003
Springer
14 years 3 months ago
A Kernighan-Lin Local Improvement Heuristic That Solves Some Hard Problems in Genetic Algorithms
We present a Kernighan-Lin style local improvement heuristic for genetic algorithms. We analyze the run-time cost of the heuristic. We demonstrate through experiments that the heur...
William A. Greene
SAC
2008
ACM
13 years 9 months ago
Dynamic populations in genetic algorithms
Biological populations are dynamic in both space and time, that is, the population size of a species fluctuates across their habitats over time. There are rarely any static or fix...
Zhanshan (Sam) Ma, Axel W. Krings
GECCO
2006
Springer
132views Optimization» more  GECCO 2006»
14 years 1 months ago
"Optimal" mutation rates for genetic search
Using a set of model landscapes we examine how different mutation rates affect different search metrics. We show that very universal heuristics, such as 1/N and the error threshol...
Jorge Cervantes, Christopher R. Stephens
TCS
2012
12 years 5 months ago
Improved simulation of nondeterministic Turing machines
Abstract. The standard simulation of a nondeterministic Turing machine (NTM) by a deterministic one essentially searches a large boundeddegree graph whose size is exponential in th...
Subrahmanyam Kalyanasundaram, Richard J. Lipton, K...
BC
2008
69views more  BC 2008»
13 years 8 months ago
The scalable mammalian brain: emergent distributions of glia and neurons
Abstract In this paper, we demonstrate that two characteristic properties of mammalian brains emerge when scaling-up modular, cortical structures. Firstly, the glia-toneuron ratio ...
Janneke F. M. Jehee, Jaap M. J. Murre