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60
Global optimization by multilevel coordinate search
 J. Global Optimization
, 1999
"... Abstract. Inspired by a method by Jones et al. (1993), we present a global optimization algorithm based on multilevel coordinate search. It is guaranteed to converge if the function is continuous in the neighborhood of a global minimizer. By starting a local search from certain good points, an impro ..."
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Cited by 73 (11 self)
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Abstract. Inspired by a method by Jones et al. (1993), we present a global optimization algorithm based on multilevel coordinate search. It is guaranteed to converge if the function is continuous in the neighborhood of a global minimizer. By starting a local search from certain good points, an improved convergence result is obtained. We discuss implementation details and give some numerical results.
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.
Adaptive Simulated Annealing for Optimization in Signal Processing Applications
, 1999
"... Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations. The adaptive simulated annealing (ASA) offers a viable optimization tool for tackling these difficult nonlinear optimization problems. Th ..."
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Cited by 31 (21 self)
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Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations. The adaptive simulated annealing (ASA) offers a viable optimization tool for tackling these difficult nonlinear optimization problems. Three applications, maximum likelihood (ML) joint channel and data estimation, infiniteimpulseresponse (IIR) filter design and evaluation of minimum symbolerrorrate (MSER) decision feedback equalizer (DFE), are used to demonstrate the effectiveness of the ASA. Keywords. Simulated annealing, global optimization, blind equalization, IIR filter, decision feedback equalizer. 1 Introduction Optimization problems with multimodal and/or nonsmooth cost functions are commonly encountered in signal processing applications. Conventional gradientbased algorithms are ineffective in these applications due to the problem of local minima or the difficulty in calculating gradients. Optimization method...
Simulated Annealing Algorithms For Continuous Global Optimization
, 2000
"... INTRODUCTION In this paper we consider Simulated Annealing algorithms (SA in what follows) applied to continuous global optimization problems, i.e. problems with the following form f = min x2X f(x); (1.1) where X ` ! n is a continuous domain, often assumed to be compact, which, combined with ..."
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Cited by 30 (1 self)
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INTRODUCTION In this paper we consider Simulated Annealing algorithms (SA in what follows) applied to continuous global optimization problems, i.e. problems with the following form f = min x2X f(x); (1.1) where X ` ! n is a continuous domain, often assumed to be compact, which, combined with the continuity or lower semicontinuity of f , guarantees the existence of the minimum value f . SA algorithms are based on an analogy with a physical phenomenon: while at high temperatures the molecules in a liquid move freely, if the temperature is slowly decreased the thermal mobility of the molecules is lost and they form a pure crystal which also corresponds to a state of minimum energy. If the temperature is decreased too quickly (the so called quenching) a liquid metal rather ends up in a polycrystalline or amorphous state with
GADO: A Genetic Algorithm For Continuous Design Optimization
, 1998
"... Genetic algorithms (GAs) have been extensively used as a means for performing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains a general purpose GA is often inefficient and unable to reach the global optimum. In this thesis we d ..."
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Cited by 28 (15 self)
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Genetic algorithms (GAs) have been extensively used as a means for performing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains a general purpose GA is often inefficient and unable to reach the global optimum. In this thesis we describe a GA for continuous designspace optimization that uses new GA operators and strategies tailored to the structure and properties of engineering design domains. Empirical results in several realistic engineering design domains as well as benchmark design domains demonstrate that using our system can greatly decrease the cost of design space search, and can also improve the quality of the resulting designs.
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.
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.
Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG
 Mathl. Computer Modelling
, 1998
"... Abstract—A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electricalchemical properties of synaptic interactions. While not useful to yield insights at the single ne ..."
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Cited by 13 (10 self)
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Abstract—A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electricalchemical properties of synaptic interactions. While not useful to yield insights at the single neuron level, SMNI has demonstrated its capability in describing largescale properties of shortterm memory and electroencephalographic (EEG) systematics. The necessity of including nonlinear and stochastic structures in this development has been stressed. Sets of EEG and evoked potential data were fit, collected to investigate genetic predispositions to alcoholism and to extract brain “signatures ” of shortterm memory. Adaptive Simulated Annealing (ASA), a global optimization algorithm, was used to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta indicators (CMI) are thereby derived for individual’s EEG data. The CMI give better signal recognition than the raw data, and can be used to advantage as correlates of behavioral states. These results give strong quantitative support for an accurate intuitive picture, portraying neocortical interactions as having common algebraic or physics mechanisms that scale across quite disparate spatial scales and functional or behavioral phenomena, i.e., describing interactions among neurons, columns of neurons, and regional masses of neurons. This paper adds to these previous investigations two important aspects, a description of how the CMI may be used in source localization, and calculations using previously ASAfitted parameters in outofsample data.
Highresolution pathintegral development of financial options
 PHYSICA A
, 2000
"... The BlackScholes theory of option pricing has been considered for many years as an important but very approximate zerothorder description of actual market behavior. We generalize the functional form of the diffusion of these systems and also consider multifactor models including stochastic volati ..."
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Cited by 12 (10 self)
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The BlackScholes theory of option pricing has been considered for many years as an important but very approximate zerothorder description of actual market behavior. We generalize the functional form of the diffusion of these systems and also consider multifactor models including stochastic volatility. Daily Eurodollar futures prices and implied volatilities are fit to determine exponents of functional behavior of diffusions using methods of global optimization, Adaptive Simulated Annealing (ASA), to generate tight fits across moving time windows of Eurodollar contracts. These shorttime fitted distributions are then developed into longtime distributions using a robust nonMonte Carlo pathintegral algorithm, PATHINT, to generate prices and derivatives commonly used by option traders.
Metaheuristics: The state of the art
 LOCAL SEARCH FOR PLANNING AND SCHEDULING
"... Metaheuristics support managers in decisionmaking with robust tools that provide highquality solutions to important applications in business, engineering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of metaheuristics. This prima ..."
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Cited by 12 (2 self)
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Metaheuristics support managers in decisionmaking with robust tools that provide highquality solutions to important applications in business, engineering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of metaheuristics. This primarily focuses on the significant progress which general frames within the metaheuristics field have implied for solving combinatorial optimization problems, mainly those for planning and scheduling.