Stochastic techniques for global optimization: A survey of recent advances

Schoen, Fabio

doi:10.1007/bf00119932

Cited by 115 publications

(53 citation statements)

References 26 publications

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“…Although some of the noisy model behavior can be explained by a reduction in the degrees of freedom, some aspects can also be explained by similarities with stochastic gradient search methods (Hassoun 1995;Hoptroff and Hall 1989), which intentionally introduce noise in gradient descent optimization. One of the advantages of this method is that it aids in finding a global minimum (Schoen 1991), seen in the unique Ks found in both of our models with noisy neurons.…”

Section: Effects Of Noisementioning

confidence: 99%

Cortical Involvement in the Recruitment of Wrist Muscles

Shah

Fagg

Barto

2004

Journal of Neurophysiology

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. Cortical involvement in the recruitment of wrist muscles. J Neurophysiol 91: 2445-2456, 2004. First published January 28, 2004 10.1152/jn.00879.2003. In executing a voluntary movement, one is faced with the problem of translating a specification of the movement in task space (e.g., a visual goal) into a muscle-recruitment pattern. Among many brain regions, the primary motor cortex (MI) plays a prominent role in the specification of movements. In what coordinate frame MI represents movement has been a topic of considerable debate. In a two-dimensional wrist step-tracking experiment, Kakei et al. described some MI cells as encoding movement in a muscle-coordinate frame and other cells as encoding movement in an extrinsic-coordinate frame. This result was interpreted as evidence for a cascade of transformations within MI from an extrinsic representation of movement to a muscle-like representation. However, we present a model that demonstrates that, given a realistic extrinsic-like representation of movement, a simple linear network is capable of representing the transformation from an extrinsic space to the muscle-recruitment patterns implementing the movements on which Kakei et al. focused. This suggests that cells exhibiting extrinsic-like qualities can be involved in the direct recruitment of spinal motor neurons. These results call into question models that presume a serial cascade of transformations terminating with MI pyramidal tract neurons that vary their activation exclusively with muscle activity. Further analysis of the model shows that the correlation between the activity of an MI neuron and a muscle does not predict the strength of the connection between the MI neuron and muscle. This result cautions against the use of correlation methods as a measure of cellular connectivity.

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Section: Effects Of Noisementioning

confidence: 99%

Cortical Involvement in the Recruitment of Wrist Muscles

Shah

Fagg

Barto

2004

Journal of Neurophysiology

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“…The global optimization method used in these experiments was a two-phase stochastic search method [38] consisting of Multistart [37] for the global phase and QNewton for local refinement, which we denote as MS-QNewton. The elements of each of the starting points used were chosen from a uniform distribution on [−5, 5], and local minimization using QNewton was then performed from each of these points.…”

Section: Comparison Of Methods On a Problem Of Varying Sizementioning

confidence: 99%

Homotopy optimization methods for global optimization.

Dunlavy¹,

O’Leary

2005

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We define a new method for global optimization, the Homotopy Optimization Method (HOM). This method differs from previous homotopy and continuation methods in that its aim is to find a minimizer for each of a set of values of the homotopy parameter, rather than to follow a path of minimizers. We define a second method, called HOPE, by allowing HOM to follow an ensemble of points obtained by perturbation of previous ones. We relate this new method to standard methods such as simulated annealing and show under what circumstances it is superior. We present results of extensive numerical experiments demonstrating performance of HOM and HOPE.

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“…A simple and effective strategy for forming a global optimiser is called the multistart [10]. A local optimiser is first defined.…”

Section: The Proposed Guided Random Search Methodsmentioning

confidence: 99%

A Search Algorithm for Global Optimisation

Chen

Wang

Harris

2005

Lecture Notes in Computer Science

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Abstract. This paper investigates a global search optimisation technique, referred to as the repeated weighted boosting search. The proposed optimisation algorithm is extremely simple and easy to implement. Heuristic explanation is given for the global search capability of this technique. Comparison is made with the two better known and widely used global search techniques, known as the genetic algorithm and adaptive simulated annealing. The effectiveness of the proposed algorithm as a global optimiser is investigated through several examples.

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Stochastic techniques for global optimization: A survey of recent advances

Cited by 115 publications

References 26 publications

Cortical Involvement in the Recruitment of Wrist Muscles

Cortical Involvement in the Recruitment of Wrist Muscles

Homotopy optimization methods for global optimization.

A Search Algorithm for Global Optimisation

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