Genetic Algorithms

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INTRODUCTION
A genetic algorithm (GA) is a procedure used to find approximate solutions to
search problems through application of the principles of evolutionary biology. Genetic
algorithms use biologically inspired techniques such as genetic inheritance, natural
selection, mutation, and sexual reproduction (recombination, or crossover). Along with
genetic programming (GP), they are one of the main classes of genetic and evolutionary
computation (GEC) methodologies.

BACKGROUND

The field of genetic and evolutionary computation (GEC) was first explored by
Turing, who suggested an early template for the genetic algorithm. Holland performed
much of the foundational work in GEC in the 1960s and 1970s. His goal of
understanding the processes of natural adaptation and designing biologically-inspired
artificial systems led to the formulation of the simple genetic algorithm (Holland, 1975). State of the field


Applications
Genetic algorithms have been applied to many classification and performance
tuning applications in the domain of knowledge discovery in databases (KDD). De Jong
et al. produced GABIL (Genetic Algorithm-Based Inductive Learning), one of the first
general-purpose GAs for learning disjunctive normal form concepts. (De Jong et al.,
1993). GABIL was shown to produce rules achieving validation set accuracy comparable
to that of decision trees induced using ID3 and C4.5.


CONCLUSION
Genetic algorithms provide a comprehensive search methodology for machine
learning and optimization. It has been shown to be efficient and powerful through many
data mining applications that use optimization and classification.