An introduction to simulated evolutionary optimization

Fogel, David B.

doi:10.1109/72.265956

Cited by 1,202 publications

(429 citation statements)

References 58 publications

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“…Evolutionary-based algorithms [10,1,9,23], are robust problems' solving techniques based on natural evolution processes. They are population-based techniques which codify a set of possible solutions to the problem, and evolve it through the application of certain evolution rules.…”

Section: Evolutionary-based Algorithmsmentioning

confidence: 99%

An Analysis of Black-Box Optimization Problems in Reinsurance: Evolutionary-Based Approaches

et al. 2013

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Black-box optimization problems (BBOP) are defined as those optimization problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program). This paper is focussed on BBOPs that arise in the field of insurance, and more specifically in reinsurance problems. In this area, the complexity of the models and assumptions considered to define the reinsurance rules and conditions produces hard black-box optimization problems, that must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in BBOP, so new computational paradigms must be applied to solve these problems. In this paper we show the performance of two evolutionary-based techniques (Evolutionary Programming and Particle Swarm Optimization). We provide an analysis in three BBOP in reinsurance, where the evolutionary-based approaches exhibit an excellent behaviour, finding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.

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Section: Evolutionary-based Algorithmsmentioning

confidence: 99%

An Analysis of Black-Box Optimization Problems in Reinsurance: Evolutionary-Based Approaches

et al. 2013

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“…The algorithms maintain a group of individuals to explore the search space. Examples of Evolutionary Computation include Genetic Algorithms (GA) 19,13], Genetic Programming (GP) 24,25], Evolutionary Programming (EP) 10,11] and Evolution Strategy (ES) 34,35]. GA uses a xed-length binary bit string as an individual.…”

Section: Backgrounds 21 Evolutionary Computationmentioning

confidence: 99%

Medical data mining using evolutionary computation

Ngan

Wong

Lam

et al. 1999

Artificial Intelligence in Medicine

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In this paper, we i n troduce a system for discovering medical knowledge by learning Bayesian networks and rules. Evolutionary Computation is used as the search algorithm. The Bayesian networks can provide an overall structure of the relationships among the attributes. The rules can capture detailed and interesting patterns in the database. The system is applied to two real-life medical databases. The knowledge discovered provides insights to and allows better understanding of the medical domains.

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“…Genetic Algorithms (GAs), are evolutionary, stochastic and global search methods. Their performance is superior to those of classical techniques [24,25] and they have been used successfully in robot path planning [26,27] . There has been little work reported involving application of this optimization method to trajectory generation for handling DLOs.…”

Section: Adjustment-motion Generation By Genetic Algorithmsmentioning

confidence: 99%

Manipulating deformable linear objects: Attachable adjustment‐motions for vibration reduction

Yue

Henrich

2001

J. Robotic Syst.

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This paper addresses the problem of handling deformable linear objects (DLOs) in a suitable way to avoid acute vibration. Different types of adjustment-motions that eliminate vibration of deformable objects and can be attached to the end of an arbitrary end-effector's trajectory are presented. For describing the dynamics of deformable linear objects, the finite element method is used to derive the dynamic differential equations. Genetic algorithm is used to find the optimal adjustment motion for each simulation example. Experiments are conducted to verify the presented manipulating method.

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An introduction to simulated evolutionary optimization

Cited by 1,202 publications

References 58 publications

An Analysis of Black-Box Optimization Problems in Reinsurance: Evolutionary-Based Approaches

An Analysis of Black-Box Optimization Problems in Reinsurance: Evolutionary-Based Approaches

Medical data mining using evolutionary computation

Manipulating deformable linear objects: Attachable adjustment‐motions for vibration reduction

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