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115
Differential Evolution  A simple and efficient adaptive scheme for global optimization over continuous spaces
, 1995
"... A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simula ..."
Abstract

Cited by 276 (4 self)
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A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. ________________________________________ 1) International Computer Science Institute, 1947 Center Street, Berkeley, CA 947041198, Suite 600, Fax: 5106437684. Email: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn Ring 6, D81739 Muenchen, Germany. Fax: 0114963644577, Email: rainer.storn@zfe.siemens.de. 2) 836 Owl Circle, Vacaville, CA 95687, kprice@solano.community.net. Introduction Problems which involve global optimiz...
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.
SoarRL: Integrating Reinforcement Learning with Soar
 Cognitive Systems
, 2005
"... In this paper, we describe an architectural modification to Soar that gives a Soar agent the opportunity to learn statistical information about the past success of its actions and utilize this information when selecting an operator. This mechanism serves the same purpose as production utilities in A ..."
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Cited by 50 (13 self)
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In this paper, we describe an architectural modification to Soar that gives a Soar agent the opportunity to learn statistical information about the past success of its actions and utilize this information when selecting an operator. This mechanism serves the same purpose as production utilities in ACTR, but the implementation is more directly tied to the standard definition of the reinforcement learning (RL) problem. The paper explains our implementation, gives a rationale for adding an RL capability to Soar, and shows results for SoarRL agents ’ performance on two tasks.
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.
Evaluating the Scalability of Distributed Systems
 IEEE Transactions on Parallel and Distributed Systems
, 2000
"... AbstractÐMany distributed systems must be scalable, meaning that they must be economically deployable in a wide range of sizes and configurations. This paper presents a scalability metric based on costeffectiveness, where the effectiveness is a function of the system's throughput and its quality of ..."
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Cited by 41 (2 self)
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AbstractÐMany distributed systems must be scalable, meaning that they must be economically deployable in a wide range of sizes and configurations. This paper presents a scalability metric based on costeffectiveness, where the effectiveness is a function of the system's throughput and its quality of service. It is part of a framework which also includes a scaling strategy for introducing changes as a function of a scale factor, and an automated virtual design optimization at each scale factor. This is an adaptation of concepts for scalability measures in parallel computing. Scalability is measured by the range of scale factors that give a satisfactory value of the metric, and good scalability is a joint property of the initial design and the scaling strategy. The results give insight into the scaling capacity of the designs, and into how to improve the design. A rapid simple bound on the metric is also described. The metric is demonstrated in this work by applying it to some wellknown idealized systems, and to real prototypes of communications software. Index TermsÐScalability, distributed systems, scalability metric, software performance, performance model, layered queuing, performance optimization, replication. 1
Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
 SIAM Journal on Optimization
, 2004
"... A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for gene ..."
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Cited by 37 (8 self)
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A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results are presented that reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are required to apply the algorithm, a hierarchy of theoretical convergence results based on the Clarke calculus is given, in which local smoothness dictate what can be proved about certain limit points generated by the algorithm. To demonstrate the usefulness of the algorithm, the algorithm is applied to the design of a loadbearing thermal insulation system. We believe this is the first algorithm with provable convergence results to directly target this class of problems.
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
Restoring low resolution structure of biological macromolecules from solution scattering using simulated annealing
 Biophys. J
, 1999
"... ABSTRACT A method is proposed to restore ab initio low resolution shape and internal structure of chaotically oriented particles (e.g., biological macromolecules in solution) from isotropic scattering. A multiphase model of a particle built from densely packed dummy atoms is characterized by a confi ..."
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Cited by 34 (8 self)
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ABSTRACT A method is proposed to restore ab initio low resolution shape and internal structure of chaotically oriented particles (e.g., biological macromolecules in solution) from isotropic scattering. A multiphase model of a particle built from densely packed dummy atoms is characterized by a configuration vector assigning the atom to a specific phase or to the solvent. Simulated annealing is employed to find a configuration that fits the data while minimizing the interfacial area. Application of the method is illustrated by the restoration of a ribosomelike model structure and more realistically by the determination of the shape of several proteins from experimental xray scattering data.
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