Results 271 to 280 of about 192,884 (311)

Evolutionary Techniques for Automation

2009
In this chapter, evolutionary techniques (ETs) will be introduced for treating automation problems in factory, manufacturing, planning and scheduling, and logistics and transportation systems. ET is the most popular metaheuristic method for solving NP-hard optimization problems.
Mitsuo Gen, Lin Lin
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A generic applied evolutionary hybrid technique

IEEE Signal Processing Magazine, 2004
In this contribution, a generic applied evolutionary hybrid technique that combines the effectiveness of adaptive multimodel partitioning filters and genetic algorithm (GAs) robustness has been designed, developed, and applied in real-world adaptive system modeling and information mining problems.
Grigorios N. Beligiannis, Lambros V. Skarlas, Spiridon Likothanasis   +2 more
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ISSR Techniques for Evolutionary Biology

2005
Inter-simple sequence repeat (ISSR) markers were originally devised for differentiating among closely related plant cultivars but have become extremely useful for studies of natural populations of plants, fungi, insects, and vertebrates. The markers are easily generated using minimal equipment and are hypervariable, yielding a large amount of data for ...
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Evolutionary Testing Techniques

2005
The development and testing of software-based systems is an essential activity for the automotive industry. 50-70 software-based systems with different complexities and developed by various suppliers are installed in today's premium vehicles, communicating with each other via different bus systems.
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Evolutionary Techniques in MEMS Synthesis

Volume 1A: 25th Biennial Mechanisms Conference, 1998
Abstract Initial results have been obtained for automatic synthesis of MEMS mask-layouts using a genetic algorithm. An initial random population of geometrically valid mask-layouts (non-self intersecting 2D polygons) is produced. The fabrication of each layout is simulated using a 3-D simulation of etching.
Hui Li, Erik K. Antonsson
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Evolutionary computation techniques and their applications

1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335), 2002
Evolutionary computation techniques, which are based on a powerful principle of evolution: survival of the fittest, constitute an interesting category of heuristic search. Evolutionary computation techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strive for survival.
Z. Michalewicz, M. Michalewicz
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Grammatical Evolutionary Techniques for Prompt Migraine Prediction

Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016
The migraine disease is a chronic headache presenting symptomatic crisis that causes high economic costs to the national health services, and impacts negatively on the quality of life of the patients. Even if some patients can feel unspecific symptoms before the onset of the migraine, these only happen randomly and cannot predict the crisis precisely ...
Josué Pagán, José L. Risco-Martín, José Manuel Moya, José L. Ayala   +3 more
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Evolutionary Techniques for Procedural Texture Automation

2013
We have developed a genetic algorithm approach for automatically generating procedural textures. Our system, known as GenShade, evaluates evolutionarily generated procedural textures by comparing their rendered images with single or multiple target images of real textures.
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A fuzzy multistage evolutionary (FUME) clustering technique

Pattern Recognition Letters, 1984
In this paper, a multistage evolutionary scheme is proposed for clustering in a large data base, like speech data. This is achieved by clustering a small subset of the entire sample set in each stage and treating the cluster centroids so obtained as samples, together with another subset of samples not considered previously, as input data to the next ...
Devi, B. Bharathi, Sarma, VVS
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Evolutionary computational techniques in electromagnetics

International Conference on Mathematical Methods in Electromagnetic Theory, 2003
The three paradigms of the evolutionary algorithms, namely, Genetic Algorithms, Evolution Strategies and Evolutionary Programming, are briefly reviewed, and the applications of the latter to optimizations of continuous as well as mixed parameter electromagnetic problems are discussed in details.
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