Population system control

Song, Jian; Deyong, Kong; Yu, Jingyuan

doi:10.1016/0895-7177(88)90443-8

Cited by 43 publications

(24 citation statements)

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“…Adjusting population size is much less popular, even though there are biological and experimental arguments to expect that this would be rewarding. In natural environments, population sizes of species change and tend to stabilize around appropriate values according some factors such as natural resources and carrying capacity of the ecosystem [17,21]. Looking at it technically, population size is the most flexible parameter in natural systems: It can be adjusted much more easily than, for instance, mutation rate.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms with On-the-Fly Population Size Adjustment

Eiben

Marchiori

Valko

2004

Lecture Notes in Computer Science

114

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Abstract. In this paper we evaluate on-the-fly population (re)sizing mechanisms for evolutionary algorithms (EAs). Evaluation is done by an experimental comparison, where the contestants are various existing methods and a new mechanism, introduced here. These comparisons consider EA performance in terms of success rate, speed, and solution quality, measured on a variety of fitness landscapes. These landscapes are created by a generator that allows for gradual tuning of their characteristics. Our test suite covers a wide span of landscapes ranging from a smooth one-peak landscape to a rugged 1000-peak one. The experiments show that the population (re)sizing mechanisms exhibit significant differences in speed, measured by the number of fitness evaluations to a solution and the best EAs with adaptive population resizing outperform the traditional genetic algorithm (GA) by a large margin.

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Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms with On-the-Fly Population Size Adjustment

Eiben

Marchiori

Valko

2004

Lecture Notes in Computer Science

114

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“…Such predictions must take into account such factors as the age distribution of the population, birth rates, death rates, and scarcity of resources as a population grows. The interested reader is referred to fuller treatments of these issues in the works of Pollard [7] and Song and Jingyuan [8].…”

Section: Resultsmentioning

confidence: 99%

Modeling Population Growth: Exponential and Hyperbolic Modeling

Hathout¹

2013

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A standard part of the calculus curriculum is learning exponential growth models. This paper, designed to serve as a teaching aid, extends the standard modeling by showing that simple exponential models, relying on two points to fit parameters do not do a good job in modeling population data of the distant past. Moreover, they provide a constant doubling time. Therefore, the student is introduced to hyperbolic modeling, and it is demonstrated that with only two population data points, an amazing amount of information can be obtained, such as reasonably accurate doubling times that are a function of t, as well as accurate estimates of such entertaining topics as the total number of people that have ever lived on earth.

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“…The model in this paper describes the population evolution of human beings with birth control, which was developed in Reference [33]. The HJB equation satisfied by the value function will be derived.…”

Section: Introductionmentioning

confidence: 99%

Numerical solution to the optimal birth feedback control of a population dynamics: viscosity solution approach

Guo¹,

Sun²

2005

Optim Control Appl Methods

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SUMMARYThis paper is concerned with the optimal birth control of a McKendrick-type age-structured population dynamic system. We use the dynamic programming approach in our investigation. The Hamilton-JacobiBellman equation satisfied by the value function is derived. It is shown that the value function is the viscosity solution of the Hamilton-Jacobi-Bellman equation. The optimal birth feedback control is found explicitly through the value function. A finite difference scheme is designed to obtain the numerical solution of the optimal birth feedback control. The validity of the optimality of the obtained control is verified numerically by comparing with different controls under the same constraint. All the data utilized in the computation are taken from the census of the population of China in 1989.

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Population system control

Cited by 43 publications

References 0 publications

Evolutionary Algorithms with On-the-Fly Population Size Adjustment

Evolutionary Algorithms with On-the-Fly Population Size Adjustment

Modeling Population Growth: Exponential and Hyperbolic Modeling

Numerical solution to the optimal birth feedback control of a population dynamics: viscosity solution approach

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