Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenological properties. It is shown, by testing them against a class of Free Fermionic models, that they are orders of magnitude more efficient than a randomised search. As an example, three generation, exophobic, Pati-Salam models with a top Yukawa occur once in every 1010 models, and yet a Genetic Algorithm can find them after constructing only 105 examples. Such non-deterministic search methods may be the only means to search for Standard Model string vacua with detailed phenomenological requirements
Genetic algorithms are search techniques that borrow ideas from the biological process of evolution....
Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]...
Data mining has as goal to extract knowledge from large databases. A database may be considered as a...
Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenol...
Abstract: Genetic Algorithms are introduced as a search method for finding string vacua with viable ...
The immensity of the string landscape and the difficulty of identifying solutions that match the obs...
Abstract In this paper, we employ genetic algorithms to explore the landscape o...
The immensity of the string landscape and the difficulty of identifying solutions that match the obs...
We introduce genetic algorithms as a means to estimate the accuracy required to discriminate among d...
This paper defines and explores a somewhat different type of genetic algorithm (GA)-a messy genetic ...
The ideal of designing a robust and efficient Genetic Algorithms (GAs), easy to use and applicable t...
This thesis discusses new approaches to string theory, aiming to open novel perspectives to connect ...
The existence of discrete properties is shown in the landscape of the Free-Fermionic Heterotic-Strin...
We introduce the modular genetic algorithm (MGA). The modular genetic algorithm is a search algorith...
A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary...
Genetic algorithms are search techniques that borrow ideas from the biological process of evolution....
Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]...
Data mining has as goal to extract knowledge from large databases. A database may be considered as a...
Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenol...
Abstract: Genetic Algorithms are introduced as a search method for finding string vacua with viable ...
The immensity of the string landscape and the difficulty of identifying solutions that match the obs...
Abstract In this paper, we employ genetic algorithms to explore the landscape o...
The immensity of the string landscape and the difficulty of identifying solutions that match the obs...
We introduce genetic algorithms as a means to estimate the accuracy required to discriminate among d...
This paper defines and explores a somewhat different type of genetic algorithm (GA)-a messy genetic ...
The ideal of designing a robust and efficient Genetic Algorithms (GAs), easy to use and applicable t...
This thesis discusses new approaches to string theory, aiming to open novel perspectives to connect ...
The existence of discrete properties is shown in the landscape of the Free-Fermionic Heterotic-Strin...
We introduce the modular genetic algorithm (MGA). The modular genetic algorithm is a search algorith...
A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary...
Genetic algorithms are search techniques that borrow ideas from the biological process of evolution....
Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]...
Data mining has as goal to extract knowledge from large databases. A database may be considered as a...