✦ LIBER ✦

A variable-length genetic algorithm for clustering and classification

✍ Scribed by R. Srikanth; R. George; N. Warsi; D. Prabhu; F.E. Petry; B.P. Buckles

Book ID: 107920036
Publisher: Elsevier Science
Year: 1995
Tongue: English
Weight: 742 KB
Volume: 16
Category: Article
ISSN: 0167-8655
DOI: 10.1016/0167-8655(95)00043-g

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

A Clustering Genetic Algorithm for Cylin

A Clustering Genetic Algorithm for Cylinder Drag Optimization

✍ Michele Milano; Petros Koumoutsakos 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 588 KB

A real coded genetic algorithm is implemented for the optimization of actuator parameters for cylinder drag minimization. We consider two types of idealized actuators that are allowed either to move steadily and tangentially to the cylinder surface ("belts") or to steadily blow/suck with a zero net

A hybrid stochastic genetic–GRASP algori

A hybrid stochastic genetic–GRASP algorithm for clustering analysis

✍ Yannis Marinakis; Magdalene Marinaki; Michael Doumpos; Nikolaos Matsatsinis; Con 📂 Article 📅 2008 🏛 Springer-Verlag 🌐 English ⚖ 301 KB

A genetic fuzzy -Modes algorithm for clu

A genetic fuzzy -Modes algorithm for clustering categorical data

✍ G. Gan; J. Wu; Z. Yang 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 154 KB

Evolving neural network using variable s

Evolving neural network using variable string genetic algorithm for color infrared aerial image classification

✍ Xiaoyang Fu; Dale Per; Shuqing Zhang 📂 Article 📅 2008 🏛 SP Science Press 🌐 English ⚖ 346 KB

Algorithms for a hashed file with variab

Algorithms for a hashed file with variable-length records

✍ Y. Manolopoulos; N. Fistas 📂 Article 📅 1992 🏛 Elsevier Science 🌐 English ⚖ 740 KB

A genetic clustering algorithm for data

A genetic clustering algorithm for data with non-spherical-shape clusters

✍ Lin Yu Tseng; Shiueng Bien Yang 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 266 KB

In solving clustering problem, traditional methods, for example, the K-means algorithm and its variants, usually ask the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. The traditional neighborhood clustering algorithm usually needs th