Genetic Algorithms

Holland, John H.

doi:10.1038/scientificamerican0792-66

Cited by 4,336 publications

(1,851 citation statements)

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“…Next, genetic operations like cross-over and muta-tion make the offspring different from their parents. This concept of the survival of the fittest, as simulated by the genetic algorithm, is a strong tool to simulate optimizing behavior (Goldberg, 1989), and is, therefore, applied to model biological and social agents (Holland and Miller, 1991;Holland, 1992;Waldrop, 1992;Janssen, 1996).…”

Section: Perspectives and Agentsmentioning

confidence: 99%

The battle of perspectives: a multi-agent model with adaptive responses to climate change

Janssen

Vries

1998

Ecological Economics

123

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To evaluate possible futures with regard to economy, energy and climate, a multi-agent modelling approach is used. Agents hold different perspectives on how the world functions (worldview) and how it should be managed (management style) and this is implemented in a simple dynamic model of the economy-energy-climate system. Each perspective is supported by a proportion of the agents, but this proportion changes in response to observations about the real world. In this way the totality of agents learn from their observations. It is concluded that this approach is a good illustration of how adaptive behavior can be included in global change modelling. Some exploratory experiments are done to address the consequences of surprises.

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Section: Perspectives and Agentsmentioning

confidence: 99%

The battle of perspectives: a multi-agent model with adaptive responses to climate change

Janssen

Vries

1998

Ecological Economics

123

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“…Holland [26] suggested that the chromosomes should be represented by binary strings. From two sets of chromosomes offspring are produced by the use of genetic operators such as crossover and mutation.…”

Section: Stochastic Methodsmentioning

confidence: 99%

“…Large penalty factors, however, lead to ill-conditioned problems. Replacing the square term in (26) by the modulus results in the exact penalty method. Here the weighting factor can be finite (…”

Section: Treatment Of Nonlinear Constraintsmentioning

confidence: 99%

Mathematical Optimization Techniques

Russenschuck¹

2010

Field Computation for Accelerator Magnets

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From the beginning the ROXIE program was structured such that mathematical optimization techniques can be applied to the design of the superconducting magnets. With the concept of features it is possible to create the complex coil assemblies in 2 and 3 dimensions with only a small number of engineering data which can then be addressed as design variables of the optimization problem. In this chapter some background information on the application of mathematical optimization techniques is given. Historical overviewMathematical optimization including numerical techniques such as linear and nonlinear programming, integer programming, network flow theory and dynamic optimization has its origin in operations research developed in world war II, e.g., Morse and Kimball 1950 [45]. Most of the real-world optimization problems involve multiple conflicting objectives which should be considered simultaneously, so-called vector-optimization problems. The solution process for vector-optimization problems is threefold, based on decision-making methods, methods to treat nonlinear constraints and optimization algorithms to minimize the objective function.Methods for decision-making, based on the optimality criterion by Pareto in 1896 [48], have been introduced and applied to a wide range of problems in economics by Marglin 1966 [42], Geoffrion 1968 [18] and Fandel 1972. The theory of nonlinear programming with constraints is based on the optimality criterion by Kuhn and Tucker, 1951 [37] -magnet. Girdinio, Molfino, Molinari and Viviani 1983 [20] optimized a profile of an electrode. These attempts tended to be application-specific, however. Only since the late 80 th, have numerical field calculation packages for both 2d and 3d applications been placed in an optimization environment. Reasons for this delay have included constraints in computing power, problems with discontinuities and nondifferentiabilities in the objective function arising from FE meshes, accuracy of the field solution and software implementation problems. A small selection of papers can be found in the references.The variety of methods applied shows that no general method exists to solve nonlinear optimization problems in computational electromagnetics in the same way that the simplex algorithm exists to 60

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“…This cannot be implemented in the current study since every particle represented a particular land use. However, instead of deleting the particles with lower fitness scores, half of them were optimised in the next stage using GA [39]. Without the moving characteristic in PSO, GA uses crossover and, especially, mutation, to jump over wide constraint regions.…”

Section: Optimisation Modulementioning

confidence: 99%

Achieving a Sustainable Urban Form through Land Use Optimisation: Insights from Bekasi City’s Land-Use Plan (2010–2030)

et al. 2017

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Cities worldwide have been trying to achieve a sustainable urban form to handle their rapid urban growth. Many sustainable urban forms have been studied and two of them, the compact city and the eco city, were chosen in this study as urban form foundations. Based on these forms, four sustainable city criteria (compactness, compatibility, dependency, and suitability) were considered as necessary functions for land use optimisation. This study presents a land use optimisation as a method for achieving a sustainable urban form. Three optimisation methods (particle swarm optimisation, genetic algorithms, and a local search method) were combined into a single hybrid optimisation method for land use in Bekasi city, Indonesia. It was also used for examining Bekasi city's land-use-plan (2010-2030) after optimising current (2015) and future land use (2030). After current land use optimisation, the score of sustainable city criteria increased significantly. Three important centres of land use (commercial, industrial, and residential) were also created through clustering the results. These centres were slightly different from centres of the city plan zones. Additional land uses in 2030 were predicted using a nonlinear autoregressive neural network with external input. Three scenarios were used for allocating these additional land uses including sustainable development, government policy, and business-as-usual. Future land use allocation in 2030 found that the sustainable development scenario showed better performance compared to government policy and business-as-usual scenarios.

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Genetic Algorithms

Cited by 4,336 publications

References 0 publications

The battle of perspectives: a multi-agent model with adaptive responses to climate change

The battle of perspectives: a multi-agent model with adaptive responses to climate change

Mathematical Optimization Techniques

Achieving a Sustainable Urban Form through Land Use Optimisation: Insights from Bekasi City’s Land-Use Plan (2010–2030)

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