Searching for authors named "Khaled Rasheed" – sorted by Relevance.
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Effect of Global Parallelism on the Behavior of a Steady State Genetic Algorithm for Design Optimization
- In this paper we investigate the effect of global parallelism using a master slave approach, on the behavior of a steady state genetic algorithm for design optimization. Empirical results in several engineering design domains demonstrate that this simple form of parallelism which has the potential f
- Cited by 3 (0 self) – Add To MetaCart
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An Incremental-Approximate-Clustering Approach for Developing Dynamic Reduced Models for Design Optimization
- In this paper we describe a method for improving genetic-algorithm-based optimization using reduced models. The main idea is to maintain a large sample of the points encountered in the course of the optimization divided into clusters. Least squares quadratic approximations are periodically formed o
- Cited by 4 (2 self) – Add To MetaCart
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An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization
- In this paper we describe a new adaptive penalty approach for handling constraints in genetic algorithm optimization problems. The idea is to start with a relatively small penalty coefficient and then increase it or decrease it on demand as the optimization progresses. Empirical results in several e
- Cited by 5 (2 self) – Add To MetaCart
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Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models
- In this paper we describe a method for improving genetic-algorithm-based optimization using informed genetic operators. The idea is to make the genetic operators such as mutation and crossover more informed using reduced models. In every place where a random choice is made, for example when a
- Cited by 9 (5 self) – Add To MetaCart
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Adaptation of Genetic Algorithms for Engineering Design Optimization
- Genetic algorithms have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains it was observed that a simple classical implementation of the GA based on binary encoding and
- Cited by 2 (2 self) – Add To MetaCart
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Guided Crossover: A New Operator for Genetic Algorithm Based Optimization
- Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient based methods which usually converge to local sub optima. However, GAs have a ten
- Cited by 3 (2 self) – Add To MetaCart
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Using Case-Based Learning to Improve Genetic-Algorithm-Based Design Optimization
- In this paper we describe a method for improving genetic-algorithm-based optimization using case-based learning. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive eval
- Cited by 6 (3 self) – Add To MetaCart
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Learning to be Selective in Genetic-Algorithm-Based Design Optimization
- In this paper we describe a method for improving genetic-algorithm-based optimization using search control. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive evaluatio
- Cited by 10 (6 self) – Add To MetaCart
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Three Dimensional Optimization of Supersonic Inlets
- This paper presents the implementation of these new design techniques and their application to a Mach 3 inlet case. The significant improvements obtained using two different optimizers are presented and compared. The results of these optimizations have been verified using a full Reynolds Averaged Na
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Effect of Global Parallelism on a Steady State GA
- In this paper we investigate the effect of global parallelism using a master slave approach, on the behavior of a steady state genetic algorithm for design optimization. 1 Introduction The abundance of powerful workstations at present makes parallelization an obvious enhancement to many optimiz
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