Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Empirical Modelling of Genetic Algorithms
Evolutionary Computation
AMP: a science-driven web-based application for the TeraGrid
Proceedings of the 5th Grid Computing Environments Workshop
A genetic algorithm applied to a main sequence stellar model
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
An efficient implementation of the backtesting of trading strategies
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
Parallelizing power systems simulation for multi-core clusters: design for an SME
HPCS'09 Proceedings of the 23rd international conference on High Performance Computing Systems and Applications
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Genetic algorithms are a class of heuristic search techniques that apply basic evolutionary operators in a computational setting. We have designed a fully parallel and distributed hardware/software implementation of the generalized optimization subroutine PIKAIA, which utilizes a genetic algorithm to provide an objective determination of the globally optimal parameters for a given model against an observational data set. We have used this modeling tool in the context of white dwarf asteroseismology, i.e., the art and science of extracting physical and structural information about these stars from observations of their oscillation frequencies: The efficient, parallel exploration of parameter-space made possible by genetic-algorithm-based numerical optimization led us to a number of interesting physical results: (1) resolution of a hitherto puzzling discrepancy between stellar evolution models and prior asteroseismic inferences of the surface helium layer mass for a DBV white dwarf; (2) precise determination of the central oxygen mass fraction in a white dwarf star; and (3) a preliminary estimate of the astrophysically important but experimentally uncertain rate for the 12C(α, γ)16O nuclear reaction. These successes suggest that a broad class of computationally intensive modeling applications could also benefit from this approach.