A successful interpretation of data goes through discovering crucial relationships between variables. Such a task can be accomplished by a Bayesian network. The dark side is that, ...
The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous ...
Peer-to-peer (P2P) topology has significant influence on the performance, search efficiency and functionality, and scalability of the application. In this paper, we present a Gen...
Geometric particle swarm optimization (GPSO) is a recently introduced formal generalization of traditional particle swarm optimization (PSO) that applies naturally to both continu...
Evolutionary Algorithms (EAs) can be used for designing Particle Swarm Optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By...
A new hybrid approach to optimization in dynamical environments called Collaborative Evolutionary-Swarm Optimization (CESO) is presented. CESO tracks moving optima in a dynamical ...
This paper presents an analysis of microarray gene expression data from patients with and without scleroderma skin disease using computational intelligence and visual data mining ...
In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergen...
Point estimates of the parameters in real world models convey valuable information about the actual system. However, parameter comparisons and/or statistical inference requires de...
Geometric particle swarm optimization (GPSO) is a recently introduced generalization of traditional particle swarm optimization (PSO) that applies to all combinatorial spaces. The...