Comparison of steady state and generational genetic algorithms for use in nonstationary environments

Vavak, Frank; Fogarty, Terence C.

doi:10.1109/icec.1996.542359

Cited by 66 publications

(47 citation statements)

References 1 publication

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“…In addition, we analyzed a generational BBO algorithm; that is, modification of each individual in the current generation occurs before any individuals are replaced in the population [14]. Future work could include the extension of our Markov and dynamic system models for other variations of BBO.…”

Section: Resultsmentioning

confidence: 99%

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A dynamic system model of biogeography-based optimization

Simon

2011

Applied Soft Computing

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Section: Resultsmentioning

confidence: 99%

“…2 as a description of one BBO generation. We perform migration and mutation for each individual in the current generation before any individuals are replaced, resulting in a generational EA [14]. The migration decision requires that the individuals be sorted in order of fitness, which is a computational consideration.…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

A dynamic system model of biogeography-based optimization

Simon

2011

Applied Soft Computing

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“…Three experiments of 60, 90 and 120 random test cases were performed. The evolutionary approach used in this paper is what is known in the literature as a 'steady state genetic algorithm' [20], in which only one new individual is produced at each generation. This means that, at each generation, there is only one new fitness evaluation.…”

Section: F1mentioning

confidence: 99%

Evolutionary testing of autonomous software agents

Nguyen

Perini

Tonella

et al. 2011

Auton Agent Multi-Agent Syst

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A system built in terms of autonomous agents may require even greater correctness assurance than one which is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents' developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimization to generate demanding test cases.

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“…The steady state genetic algorithm is used to recover the shape and the conductivity of the scatterer. It is found the steady-state genetic algorithm [12,13] can reduce the calculation time of the image problem compared with the generational genetic algorithm. In Section 2, the theoretical formulation for the electromagnetic imaging is presented.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Imaging for an Imperfectly Conducting Cylinder Buried in a Three-Layer Structure by the Genetic Algorithm

Lee¹,

Chiu²,

Lin³

2004

PIER

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Abstract-The imaging of an imperfectly conducting cylinder buried in a three-layer structure by the genetic algorithm is investigated. An imperfectly conducting cylinder of unknown shape and conductivity buried in the second layer scatters the incident wave from the first layer or the third layer. We measure the scattered field in the first and third layers. Based on the boundary condition and the recorded scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization problem. The genetic algorithm is then employed to find out the global extreme solution of the cost function. Numerical results demonstrated that, even when the initial guess is far away from the exact one, good reconstruction can be obtained. In such a case, the gradient-based methods often get trapped in a local extreme. In addition, the effect of uniform noise on the reconstruction is investigated.

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Comparison of steady state and generational genetic algorithms for use in nonstationary environments

Cited by 66 publications

References 1 publication

A dynamic system model of biogeography-based optimization

A dynamic system model of biogeography-based optimization

Evolutionary testing of autonomous software agents

Electromagnetic Imaging for an Imperfectly Conducting Cylinder Buried in a Three-Layer Structure by the Genetic Algorithm

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