Feature selection refers to the problem of selecting relevant features which
produce the most predictive outcome. In particular, feature selection task is
involved in datasets containing huge number of features. Rough set theory has
been one of the most successful methods used for feature selection. However,
this method is still not able to find optimal subsets. This paper proposes a
new feature selection method based on Rough set theory hybrid with Bee Colony
Optimization (BCO) in an attempt to combat this. This proposed work is applied
in the medical domain to find the minimal reducts and experimentally compared
with the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methods
such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle
Swarm Optimization (PSO).
Compare the proposed firefly algorithm with other metaheuristic algorithms such
As particle swarm optimization (PSO). Simulations and results indicate that the
Proposed firefly algorithm is superior to existing metaheuristic algorithms.
Finally we will discuss its applications and implicatio...
We develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule. This algorithm is compared to the standard CMA-ES. The elitist CMA-ES turns out to be slightly faster o...
In this paper, the uplink direct sequence code division multiple access
(DS-CDMA) multiuser detection problem (MuD) is studied into heuristic
perspective, named particle swarm optimization (PSO). Regarding different
system improvements for future technologies, such as high-order modulation and
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