GECCO
2005
Springer
13 years 10 months ago
2005
Springer
This paper presents an efficient technique of designing twodimensional IIR digital filters using a new algorithm involving the tightly coupled synergism of particle swarm optimiza...
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. In this paper we present two new, improved variants of D...
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
This paper introduces a novel scheme of improving the performance of particle swarm optimization (PSO) by a vector differential operator borrowed from differential evolution (DE)....
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
XCS is a flexible system for data mining due to its ability to deal with environmental changes, learn online with little prior knowledge and evolve accurate and maximally general...
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
This paper presents a comprehensive, multivariate account of how initial population material is used over the course of a genetic programming run as while various factors influenc...
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
Optimization problems such as resource allocation, job-shop scheduling, equipment utilization and process scheduling occur in a broad range of processing industries. This paper pr...
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
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...
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
This research presents an analysis of the reported successes of the Cartesian Genetic Programming method on a simplified form of the Boolean parity problem. We show the method of...
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
This paper outlines a radial basis function neural network approach to predict the failures in overhead distribution lines of power delivery systems. The RBF networks are trained ...