What is genetic algorithm? Explain its importance in problem solving ?

-->

Genetic Algorithms – An Overview

Genetic algorithms are increasingly popular for solving optimization, search and machine learning problems. The analog between optimizing a fitness function and biological processes of natural selection and genetics is generally attributed to John H. Holland and his students at the University of Michigan.  His landmark publication “Adaptation in Natural and Artificial Systems” (Holland, 1975) sparked wide ranging investigations into his approach in a variety of areas ranging from science and engineering to business.

This genetic algorithm implementation supports Holland's basic algorithm with most popular variations.  This is achieved by supporting:

1.     User defined population size and selection method including roulette, remainder, tournament and stochastic universal sampling both with and without replacement,

2.     Random or user defined initial populations,

3.     Any combination of four different data types:  nominal, binary, integer and real,

4.     Base 2 and Gray encoding and decoding of integer and real data types,

5.     Automatic encoding and decoding of chromosome information into phenotypes,

6.     User specified number of crossover points and three different options for crossover: standard, inversion and partially matched crossover,

7.     Elitism to ensure fittest individuals are retained between generations,

8.     User supplied fitness functions with or without additional function parameters,

9.     User defined crossover and mutation probabilities,oo

10.   Linear and sigma fitness scaling,

11.   Customized and predetermined stopping criteria,

12.   Measures of algorithm convergence and efficiency – velocity, on-line and off-line fitness.