Genetic Algorithm for the Double Digest Problem in Genetics.

Date of Submission

December 2002

Date of Award

Winter 12-12-2003

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Machine Intelligence Unit (MIU-Kolkata)


Murthy, C. A. (MIU-Kolkata; ISI)

Abstract (Summary of the Work)

1.1 MotivationOver the last decade, the progress in the filed of genetics has been phenomenal. In particular, the gene mapping and sequencing problem have drawn the attention of mathematicians and computer scicntists with the consequence of the emerging field of computational molecular biology (1]. Many problems related to identification and analysis of physical sequencing of DNA molecules have been formulated very elegantly as combinatorial problems and even been proven to be NP-hard. Herein lies the necessity for designing efficient heuristics. Genetic algorithms have been found to be very successful in solving computationally complex problems. Thus the motivation for applying genetic algorithms to combinatorially hard problems in genetics arose.1.2 What are Genetic Algorithms?GA is one of the most promising tools in the Soft Computing domain. Due to its versatile ability to tackle optimization problems arising from almost every branch of science and technology it has become an important tool to solve ‘hard’ problems nowadays [2, 3]. With GAs having such versatile abilities, one might think that the inner workings of a GA would be very complex.In fact, the opposite is true. Simple GAs are mainly based on simple string alteration and substring concatenation,nothing more, nothing less. Even more complex versions of GAS still use these basic ideas as the core of their search engines.BEGIN AGACreate initial population at random.WHILE NOT stop DOBEGINNatural seleetion: Select parents from the population.Cruss over: Produce children from the selected parents.Mutation: Mutate individual in the population.Restore the chilcdren for the next generation population.END Output the best individual found.END AGA.Figure 1.1: Structure of Genetic Algorithm.1.2.1 Structure of a Genetic AlgorithmFor applying GA on some particular optimization problem the Representation of strings to encode the search space and an appropriate Cost Function are to be judiciously designed before hand. Once the string representation and cost function are fixed, a set of initially chosen strings are subjected to a set of genetic operations,namely nutural selection, cross over and mutation to olbtain the set of next generation strings. Each operation is associated with a non- zero probability of being applied. Further details of these operators appear in section L8. The same process is repeated for some pre-delined number of times. Over the generations the process converges and the genetic operations give birth to the optimum strings. The pseudo-code of the abstract genetic algorithm is furnished in Figure Stochastic nature and power of Genetic Algorithm .Many of the combinatorial optimization problems having substantial practical importance, are known to be computationally hard. The inherent stochastic nature of Genetic Algorithm is found suitable in solving such hard problems. As the operations of the genetic algorithm are very simple and can explore the entire search space very efficiently, GAs are used extensively on computationally hard optimization problems to get the near optimal result in considerably small time.1.2.3 Simple and Elitist modelsGAs are broadly categorized into two models as follows: Simple model of GA These are GAs consisting of only the above mentioned Genetic Operators namely natural selection, cross over and mutation.


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Creative Commons Attribution 4.0 International License
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