Journals Information
Computational Research(CEASE PUBLICATION) Vol. 2(2), pp. 21 - 26
DOI: 10.13189/cr.2014.020202
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Better Implementation of Evolutionary Algorithm through Mutant Function
Paras Nath Singh 1,*, Vikram Singh 2, Kumar Anand 3
1 GIST (Engg. College), Rayagada, Odisha, India
2 Dover鈥檚 Solutions, Bangalore, Karnataka, India
3 Marwadi Educational Foundation, FOE, Rajkot, Gujrat
ABSTRACT
Evolutionary algorithm is a probabilistic counterpart to a deterministic search method that impersonates the representation of natural biological evolution. Evolutionary algorithm (EA) operates on a population of potential solutions applying the principle of survival of the fittest to produce better and better estimates to a solution. At each generation, a new set of guesses is created by the process of selecting individuals according to their level of fitness in the problem domain and upbringing them together using operators copied from natural genetics. Evolutionary programming is similar to genetic programming, but the structure of the program is secure and its numerical parameters are allowed to change. The concept leads to the evolution of populations of individuals that are better suited to their environment than the individuals that they were created from, just as in natural adaptation. Mutability means for objects which can be changed and a mutant function mutates the object. In this paper we target a string and an array of random characters chosen from the set of upper-case alphabets together with the space, and of the same length as the target string. A fitness function computes the 鈥榗loseness鈥� of its argument to the target string. A mutant function with a string and a mutation rate returns a copy of the string, with some characters mutated. Finally after several iteration it "mutates " to target string successfully.
KEYWORDS
Evolutionary Algorithm, Mutation and Mutant Function, Fitness Function
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Paras Nath Singh , Vikram Singh , Kumar Anand , "Better Implementation of Evolutionary Algorithm through Mutant Function," Computational Research(CEASE PUBLICATION), Vol. 2, No. 2, pp. 21 - 26, 2014. DOI: 10.13189/cr.2014.020202.
(b). APA Format:
Paras Nath Singh , Vikram Singh , Kumar Anand (2014). Better Implementation of Evolutionary Algorithm through Mutant Function. Computational Research(CEASE PUBLICATION), 2(2), 21 - 26. DOI: 10.13189/cr.2014.020202.