## Genetic Algorithm in Search and Optimization: The Technique and Applications (1997)

Venue: | Proc. of Int. Workshop on Soft Computing and Intelligent Systems |

Citations: | 6 - 0 self |

### BibTeX

@INPROCEEDINGS{Deb97geneticalgorithm,

author = {Kalyanmoy Deb},

title = {Genetic Algorithm in Search and Optimization: The Technique and Applications},

booktitle = {Proc. of Int. Workshop on Soft Computing and Intelligent Systems},

year = {1997},

pages = {58--87}

}

### OpenURL

### Abstract

A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary string structures. Every solution is assigned a fitness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modified to a new population by applying three operators similar to natural genetic operators---reproduction, crossover, and mutation. A GA works iteratively by successively applying these three operators in each generation till a termination criterion is satisfied. Over the past one decade, GAs have been successfully applied to a wide variety of problems, because of their simplicity, global perspective, and inherent parallel processing. In this paper, we outline the working principle of a GA by describing these three operators and by outlining an intuitive sketch ...

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Citation Context ...ue so that solutions with smaller objective function value get larger fitness. Usually, the following transformation function is used for minimization problems: Fitness = 1 1 + f(x 1 ; : : : ; xN ) : =-=(3)-=- There are a number of advantages of using a string representation to code variables. First, this allows a shielding between the working of GA and the actual problem. What GA processes is `-bit string... |

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Citation Context ...search algorithm. 4.2 A Simple Simulation To illustrate the working of GA operators, we consider a simple sinusoidal function that is to be maximized (Deb, 1996): Maximize sin(x) Variable bound 0sxs: =-=(4)-=- For the illustration purpose, we use 5-bit binary strings to represent the variable x, so that there are only 2 5 or 32 strings in the search space. We use the linear mapping rule (Equation 1) betwee... |

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