Add to ORKGThe Gaussian Q-function is the integral of the tail of the Gaussian distribution; as such, it is important across a vast range of fields requiring stochastic analysis. No elementary closed form is possible, so a number of approximations have been proposed. We use a Genetic Programming (GP) sys-tem, Tree Adjoining Grammar Guided GP (TAG3P) with local search operators to evolve approximations of the Q-function in the form given by Benitez [1]. We found more ac-curate approximations than any previously published. This confirms the practical importance of local search in TAG3P
Genetic Programming (GP) is a powerful string processing technique based on the Darwinian paradigm o...
This paper is posted here with permission from IEEE - Copyright @ 2007 IEEEThis paper proposes a sel...
The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete f...
This article presents new and accurate approximations for the Gaussian Q-function, which is an impor...
This paper presents a new approach that combines two well-known approximations in order to improve t...
This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the muta...
This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the muta...
In this paper, as an extension of a previous study, an improved approximation for the Gaussian Q-fun...
We revisit the Karagiannidis–Lioumpas (KL) approximation of the Q -function by optimizing its coeffi...
This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe use of evolutionary...
In this paper, we present a comprehensive overview of (perhaps) all possible approximations resultin...
When using genetic programming (GP) or other techniques that try to approximate unknown functions, t...
The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for...
This article is posted here with permmission from IEEE - Copyright @ 2010 IEEEEvolution strategies w...
When using genetic programming (GP) or other techniques that try to approximate unknown functions, t...
Genetic Programming (GP) is a powerful string processing technique based on the Darwinian paradigm o...
This paper is posted here with permission from IEEE - Copyright @ 2007 IEEEThis paper proposes a sel...
The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete f...
This article presents new and accurate approximations for the Gaussian Q-function, which is an impor...
This paper presents a new approach that combines two well-known approximations in order to improve t...
This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the muta...
This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the muta...
In this paper, as an extension of a previous study, an improved approximation for the Gaussian Q-fun...
We revisit the Karagiannidis–Lioumpas (KL) approximation of the Q -function by optimizing its coeffi...
This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe use of evolutionary...
In this paper, we present a comprehensive overview of (perhaps) all possible approximations resultin...
When using genetic programming (GP) or other techniques that try to approximate unknown functions, t...
The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for...
This article is posted here with permmission from IEEE - Copyright @ 2010 IEEEEvolution strategies w...
When using genetic programming (GP) or other techniques that try to approximate unknown functions, t...
Genetic Programming (GP) is a powerful string processing technique based on the Darwinian paradigm o...
This paper is posted here with permission from IEEE - Copyright @ 2007 IEEEThis paper proposes a sel...
The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete f...