One of the classic data mining problems is discovery of frequent itemsets. This problem particularly attracts database community as it resembles traditional database querying. In t...
Maciej Zakrzewicz, Mikolaj Morzy, Marek Wojciechow...
This paper presents a genetic algorithm (GA) for Kmeans clustering. Instead of the widely applied stringof-group-numbers encoding, we encode the prototypes of the clusters into th...
This paper presents a modularization strategy for linear genetic programming (GP) based on a substring compression/substitution scheme. The purpose of this substitution scheme is t...
In order to overcome the low convergence speed and prematurity of classical genetic algorithm, an improved method named directional self-learning of genetic algorithm (DSLGA) is p...
Two efficient clustering-based genetic algorithms are developed for the optimisation of reaction rate parameters in chemical kinetic modelling. The genetic algorithms employed are ...
Lionel Elliott, Derek B. Ingham, Adrian G. Kyne, N...