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33
Simulated annealing: Practice versus theory
 Mathl. Comput. Modelling
, 1993
"... this paper "ergodic" is used in a very weak sense, as it is not proposed, theoretically or practically, that all states of the system are actually to be visited ..."
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Cited by 156 (20 self)
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this paper "ergodic" is used in a very weak sense, as it is not proposed, theoretically or practically, that all states of the system are actually to be visited
Genetic Algorithms And Very Fast Simulated Reannealing: A Comparison
, 1992
"... We compare Genetic Algorithms (GA) with a functional search method, Very Fast Simulated Reannealing (VFSR), that not only is efficient in its search strategy, but also is statistically guaranteed to find the function optima. GA previously has been demonstrated to be competitive with other standard B ..."
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Cited by 100 (18 self)
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We compare Genetic Algorithms (GA) with a functional search method, Very Fast Simulated Reannealing (VFSR), that not only is efficient in its search strategy, but also is statistically guaranteed to find the function optima. GA previously has been demonstrated to be competitive with other standard Boltzmanntype simulated annealing techniques. Presenting a suite of six standard test functions to GA and VFSR codes from previous studies, without any additional fine tuning, strongly suggests that VFSR can be expected to be orders of magnitude more efficient than GA.
Adaptive simulated annealing (ASA): Lessons learned
 Control and Cybernetics
, 1996
"... Adaptive simulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more efficiently than by using other previous simulated annealing algorithms. The author's ASA code has been publicly available for over two years. Durin ..."
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Cited by 72 (14 self)
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Adaptive simulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more efficiently than by using other previous simulated annealing algorithms. The author's ASA code has been publicly available for over two years. During this time the author has volunteered to help people via email, and the feedback obtained has been used to further develop the code.
Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading
 MATH. MODELLING
, 1996
"... A paradigm of statistical mechanics of financial markets (SMFM) using nonlinear nonequilibrium algorithms, first published in L. Ingber, Mathematical Modelling, 5, 343361 (1984), is fit to multivariate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to p ..."
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Cited by 41 (34 self)
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A paradigm of statistical mechanics of financial markets (SMFM) using nonlinear nonequilibrium algorithms, first published in L. Ingber, Mathematical Modelling, 5, 343361 (1984), is fit to multivariate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta are thereby derived and used as technical indicators in a recursive ASA optimization process to tune trading rules. These trading rules are then used on outofsample data, to demonstrate that they can profit from the SMFM model, to illustrate that these markets are likely not efficient.
Statistical mechanics of multiple scales of neocortical interactions
 in Neocortical Dynamics and Human EEG Rhythms, (Edited by P.L. Nunez
, 1995
"... 14. Statistical mechanics of multiple scales of neocortical interactions ..."
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Cited by 36 (18 self)
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14. Statistical mechanics of multiple scales of neocortical interactions
Statistical mechanics of neocortical interactions. EEG dispersion relations
 IEEE Trans. Biomed. Eng
, 1985
"... Abstract—An approach is explicitly formulated to blend a local with a global theory to investigate oscillatory neocortical firings, to determine the source and the informationprocessing nature of the alpha rhythm. The basis of this optimism is founded on a statistical mechanical theory of neocortica ..."
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Cited by 29 (27 self)
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Abstract—An approach is explicitly formulated to blend a local with a global theory to investigate oscillatory neocortical firings, to determine the source and the informationprocessing nature of the alpha rhythm. The basis of this optimism is founded on a statistical mechanical theory of neocortical interactions which has had success in numerically detailing properties of shorttermmemory (STM) capacity at the mesoscopic scales of columnar interactions, and which is consistent with other theory deriving similar dispersion relations at the macroscopic scales of electroencephalographic (EEG) and magnetoencephalographic (MEG) activity.
Generic Mesoscopic Neural Networks Based on Statistical Mechanics of Neocortical Interactions
 Bull. Am. Phys. Soc
, 1992
"... able nonlinear stochastic development [113]. The basic approach of the SMNI has been to statistically aggregate synaptic and neuronal interactions, from microscopic systematics to mesoscopic interactions among minicolumns of hundreds of neurons, to macroscopic macrocolumnar interactions among thous ..."
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Cited by 28 (23 self)
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able nonlinear stochastic development [113]. The basic approach of the SMNI has been to statistically aggregate synaptic and neuronal interactions, from microscopic systematics to mesoscopic interactions among minicolumns of hundreds of neurons, to macroscopic macrocolumnar interactions among thousands of minicolumns, to approach regional spatial scales of several millimeters to several centimeters. The theory has been tested by verifying observations at the mesoscopic scale, e.g., shortterm memory phenomena [4,6], and at the macroscopic scale, e.g., electroencephalography (EEG) [4,5,13]. A current description of the theory to date is in Ref. [13], where extensions were made to the SMNI to correlate human behavioral states to circuitries measured by EEG electrode recordings, an ongoing project. While the experimental resolution of EEG typically is on the order of several centimeters, new work has shown that under some circumstances this resolution can be sharpened to several millimet
Adaptive Simulated Annealing (ASA)
"... Adaptive Simulated Annealing (ASA) is a Clanguage code developed to statistically find the best global fit of a nonlinear constrained nonconvex costfunction over aDdimensional space. This algorithm permits an annealing schedule for “temperature ” T decreasing exponentially in annealingtime k, T ..."
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Cited by 25 (2 self)
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Adaptive Simulated Annealing (ASA) is a Clanguage code developed to statistically find the best global fit of a nonlinear constrained nonconvex costfunction over aDdimensional space. This algorithm permits an annealing schedule for “temperature ” T decreasing exponentially in annealingtime k, T = T 0 exp(−ck 1/D). The introduction of reannealing also permits adaptation to changing sensitivities in the multidimensional parameterspace. This annealing schedule is faster than fast Cauchy annealing, where T = T 0/k, and much faster than Boltzmann annealing, where T = T 0/lnk. ASA has over 100 OPTIONS to provide robust tuning over many classes of nonlinear stochastic systems.
Statistical mechanics of neocortical interactions: Multiple scales of EEG
, 1993
"... The statistical mechanics of neocortical interactions (SMNI) approach derives a theoretical model for aggregated neuronal activity that defines the “dipole” assumed by many EEG researchers. This defines a nonlinear stochastic filter to extract EEG signals. ..."
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Cited by 19 (19 self)
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The statistical mechanics of neocortical interactions (SMNI) approach derives a theoretical model for aggregated neuronal activity that defines the “dipole” assumed by many EEG researchers. This defines a nonlinear stochastic filter to extract EEG signals.
Canonical momenta indicators of financial markets and neocortical
 EEG.” InInternational Conference on Neural Information Processing (ICONIP’96
, 1996
"... Abstract—A paradigm of statistical mechanics of financial markets (SMFM) is fit to multivariate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabi ..."
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Cited by 16 (16 self)
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Abstract—A paradigm of statistical mechanics of financial markets (SMFM) is fit to multivariate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta are thereby derived and used as technical indicators in a recursive ASA optimization process to tune trading rules. These trading rules are then used on outofsample data, to demonstrate that they can profit from the SMFM model, to illustrate that these markets are likely not efficient. This methodology can be extended to other systems, e.g., electroencephalography. This approach to complex systems emphasizes the utility of blending an intuitive and powerful mathematicalphysics formalism to generate indicators which are used by AItype rulebased models of management. 1.