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Selective evolutionary generation sy...

Menezes, Amor A.

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Selective evolutionary generation systems: Theory and applications.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Selective evolutionary generation systems: Theory and applications./
Author:	Menezes, Amor A.
Description:	169 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-11, Section: B, page: .
Contained By:	Dissertation Abstracts International71-11B.
Subject:	Engineering, Aerospace. -
Online resource:	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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020 $a 9781124278858
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100 1 $a Menezes, Amor A. $3 1671114
245 1 0 $a Selective evolutionary generation systems: Theory and applications.
300 $a 169 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-11, Section: B, page: .
500 $a Adviser: Pierre T. Kabamba.
502 $a Thesis (Ph.D.)--University of Michigan, 2010.
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.
520 $a Applications in three areas illustrate the versatility of the SECS theory: flight mechanics, control of dynamic systems, and artificial intelligence. The dissertation results touch upon several open problems in the fields of artificial life, complex systems, artificial intelligence, and robotics.
590 $a School code: 0127.
650 4 $a Engineering, Aerospace. $3 1018395
650 4 $a Engineering, Robotics. $3 1018454
650 4 $a Artificial Intelligence. $3 769149
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690 $a 0771
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710 2 $a University of Michigan. $3 777416
773 0 $t Dissertation Abstracts International $g 71-11B.
790 1 0 $a Kabamba, Pierre T., $e advisor
790 $a 0127
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3429298