Angeline, P.J.: Using selection to improve particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998), Anchorage, Alaska (1998) Home/Search Document Not in Database Summary Related Articles Check |
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Robust Evolutionary Algorithms with Toroidal Search Space.. - Someya (Correct)
....1: The sampling biases of BLX a and UNDX formly. In this case, most crossover operators, such as BLX a [5] Unimodal Normal Distribution Crossover (UNDX) 10] Center of Mass Crossover (CMX) 17] Simplex Crossover (SPX) 19] like to search the center of search space much more than the other [1, 4, 9,18]. This bias is called Sampling Bias [4, 18] Fig. 1 ex plains the sampling biases of BLX a and UNDX. The horizontal axis is domain of definition. The verti cal axis is theoretical probability density of generating children when a crossover produces them from a pair of parents, chosen out of ....
P. J. Angeline. Using Selection to Improve Particle [16] Swarm Optimization. In Proc. of the ICEC'98, pages 84-89, 1998.
Genetic Algorithm with Search Area Adaptation for the Function - Its (2001) (Correct)
....searches in the area intensively using mutation. 5.2 Experiments without Sampling Bias In this section, we perform extra experiments without sampling bias. It has been reported that most of crossover operators for real coded GAs like to search the center of search space much more than the other [1, 20]. This bias is called sampling bias . Figure 7 explains the sampling biases of UNDX and TMX. The horizontal axis is domain of definition. The vertical axis is theo retical probability density of generating children when a crossover produces them from a pair of parents, chosen out of the ....
P. J. Angeline. Using Selection to Improve Particle Swarm Optimization. In Proc. of the ICEC'98, pages 84-89, 1998.
Cooperative Learning in Neural Networks using Particle.. - van den Bergh.. (2000) (2 citations) (Correct)
....Swarm Optimizer. Work done by Eberhart and Shi [9, 4] focus on optimizing the update equations for the particles. Their improvements generally leads to better performance on a very large class of problems. These improvements can be (and have been) applied to the technique presented here. Angeline [1] used a selection mechanism (as used in genetic algorithms) in an attempt to improve the general quality of the particles in a swarm. This approach lead to improved performance on some problems, but somewhat worse performance on others. Kennedy [7] used cluster analysis to modify the update ....
P.J. Angeline. Using Selection to Improve Particle Swarm Optimization. In Proceedings of IJCNN'99, pages 84--89, Washington, USA, July 1999.
From Nanotechnology to Nano-Planning - Czarn, MacNish (1998) (1 citation) (Correct)
....the use of adaptive planning or case based planning algorithms [49, 36] will expedite the process of finding near optimal synthetic pathways. At present, little research has been carried out on the application of adaptive algorithms, such as genetic algorithms or particle swarm optimization (PSO) [41, 4] in automated planning [10, 48] This will form a major component of research into nano planning. 2.5 Nano Robotic Fine Motor Control Software will be needed to control the mechanical movement of nano robots which will carry out a set of path actions leading to the completion of a goal task ....
Angeline, P. J. Using selection to improve particle swarm optimization. In Proc. 1998 IEEE World Congress on Computational Intelligence, pp. 84--89, Anchorage: Alaska, May 1998. IEEE.
The Kalman Swarm - New Approach To (Correct)
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Angeline, P.J.: Using selection to improve particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998), Anchorage, Alaska (1998)
Improving on the Kalman Swarm - Extracting Its Essential (Correct)
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Angeline, P.J.: Using selection to improve particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998), Anchorage, Alaska (1998)
Extending Particle Swarm Optimisation via Genetic Programming - Poli, Langdon, Holland (2005) (Correct)
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P. J. Angeline. Using selection to improve particle swarm optimization. In IEEE World Congress on computational intelligence, ICEC-98, pages 84--89, Anchorange, Alaska, 1998.
Exposing Origin-Seeking Bias in PSO - Monson, Seppi (2005) (Correct)
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P. J. Angeline. Using selection to improve particle swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998.
The Kalman Swarm: A New Approach to Particle Motion in Swarm.. - Monson, Seppi (2004) (Correct)
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Angeline, P.J.: Using selection to improve particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998), Anchorage, Alaska (1998)
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