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» Self Adaptation of Operator Rates in Evolutionary Algorithms
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AE
2005
Springer
15 years 3 months ago
Algorithms (X, sigma, eta): Quasi-random Mutations for Evolution Strategies
Randomization is an efficient tool for global optimization. We here define a method which keeps : – the order 0 of evolutionary algorithms (no gradient) ; – the stochastic as...
Anne Auger, Mohamed Jebalia, Olivier Teytaud
AHS
2006
IEEE
142views Hardware» more  AHS 2006»
15 years 3 months ago
On-Chip Evolution Using a Soft Processor Core Applied to Image Recognition
To increase the flexibility of single-chip evolvable hardware systems, we explore possibilities of systems with the evolutionary algorithm implemented in software on an onchip pr...
Kyrre Glette, Jim Torresen, Moritoshi Yasunaga, Yo...
GECCO
2005
Springer
142views Optimization» more  GECCO 2005»
15 years 3 months ago
Toward evolved flight
We present the first hardware-in-the-loop evolutionary optimization on an ornithopter. Our experiments demonstrate the feasibility of evolving flight through genetic algorithms an...
Rusty Hunt, Gregory Hornby, Jason D. Lohn
CEC
2008
IEEE
15 years 4 months ago
Auto-tuning fuzzy granulation for evolutionary optimization
—Much of the computational complexity in employing evolutionary algorithms as optimization tool is due to the fitness function evaluation that may either not exist or be computat...
Mohsen Davarynejad, Mohammad R. Akbarzadeh-Totonch...
GPEM
2002
95views more  GPEM 2002»
14 years 9 months ago
On Appropriate Adaptation Levels for the Learning of Gene Linkage
A number of algorithms have been proposed aimed at tackling the problem of learning "Gene Linkage" within the context of genetic optimisation, that is to say, the problem...
James Smith