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Selective evolutionary generation sy...

Menezes, Amor A.

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Selective evolutionary generation systems: Theory and applications.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Selective evolutionary generation systems: Theory and applications./
作者:	Menezes, Amor A.
面頁冊數:	169 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-11, Section: B, page: .
Contained By:	Dissertation Abstracts International71-11B.
標題:	Engineering, Aerospace. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3429298
ISBN:	9781124278858

Selective evolutionary generation systems: Theory and applications.
Menezes, Amor A.

Selective evolutionary generation systems: Theory and applications. - 169 p.

Source: Dissertation Abstracts International, Volume: 71-11, Section: B, page: .

Thesis (Ph.D.)--University of Michigan, 2010.

This dissertation is devoted to the problem of behavior design, which is a generalization of the standard global optimization problem: instead of generating the optimizer, the generalization produces, on the space of candidate optimizers, a probability density function referred to as the behavior. The generalization depends on a parameter, the level of selectivity, such that as this parameter tends to infinity, the behavior becomes a delta function at the location of the global optimizer. The motivation for this generalization is that traditional off-line global optimization is non-resilient and non-opportunistic. That is, traditional global optimization is unresponsive to perturbations of the objective function. On-line optimization methods that are more resilient and opportunistic than their off-line counterparts typically consist of the computationally expensive sequential repetition of off-line techniques. A novel approach to inexpensive resilience and opportunism is to utilize the theory of Selective Evolutionary Generation Systems (SECS), which sequentially and probabilistically selects a candidate optimizer based on the ratio of the fitness values of two candidates and the level of selectivity. Using time-homogeneous, irreducible, ergodic Markov chains to model a sequence of local, and hence inexpensive, dynamic transitions, this dissertation proves that such transitions result in behavior that is called rational; such behavior is desirable because it can lead to both efficient search for an optimizer as well as resilient and opportunistic behavior. The dissertation also identifies system-theoretic properties of the proposed scheme, including equilibria, their stability and their optimality. Moreover, this dissertation demonstrates that the canonical genetic algorithm with fitness proportional selection and the (1+1) evolutionary strategy are particular cases of the scheme.

ISBN: 9781124278858Subjects--Topical Terms:

1018395
Engineering, Aerospace.

Selective evolutionary generation systems: Theory and applications.
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520 $a This dissertation is devoted to the problem of behavior design, which is a generalization of the standard global optimization problem: instead of generating the optimizer, the generalization produces, on the space of candidate optimizers, a probability density function referred to as the behavior. The generalization depends on a parameter, the level of selectivity, such that as this parameter tends to infinity, the behavior becomes a delta function at the location of the global optimizer. The motivation for this generalization is that traditional off-line global optimization is non-resilient and non-opportunistic. That is, traditional global optimization is unresponsive to perturbations of the objective function. On-line optimization methods that are more resilient and opportunistic than their off-line counterparts typically consist of the computationally expensive sequential repetition of off-line techniques. A novel approach to inexpensive resilience and opportunism is to utilize the theory of Selective Evolutionary Generation Systems (SECS), which sequentially and probabilistically selects a candidate optimizer based on the ratio of the fitness values of two candidates and the level of selectivity. Using time-homogeneous, irreducible, ergodic Markov chains to model a sequence of local, and hence inexpensive, dynamic transitions, this dissertation proves that such transitions result in behavior that is called rational; such behavior is desirable because it can lead to both efficient search for an optimizer as well as resilient and opportunistic behavior. The dissertation also identifies system-theoretic properties of the proposed scheme, including equilibria, their stability and their optimality. Moreover, this dissertation demonstrates that the canonical genetic algorithm with fitness proportional selection and the (1+1) evolutionary strategy are particular cases of the scheme.
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