Prediction of Protein Structure using Parallel Genetic Algorithm

Abstract

Genetic Algorithms are generally used to draw a similarity between the Genetic mutation and Cross Over within populations from the field of biology. Genetic algorithms are highly and significantly parallel in nature and performance. These types of algorithms can be used to solve many other important problems such as the Graph Partitioning problem that deals with partitioning of graph, the famous Travelling salesman problems etc. Implementation of these algorithm shows a trade-off between Genetic search capable qualities and execution performance qualities. In this paper we worked in order to improvise the execution performance rate of algorithms, those particular implementations with lesser communications between populations are considered best and highly efficient. In this same direction, we tried to present an algorithm using discrete small subpopulation groups. Therefore, this particular implementation tries to reduce the quality of search of the algorithm. Thus, we tried to improve the quality of this type of search by having a centralized population system. Here, we analyzed some of the other alternatives for the implementation of these algorithms on distributed memory architectures in which centralized data can be significantly implemented. Prediction of tertiary protein structure is also presented in the paper as an example in which we tried to implement these alternatives of parallel algorithms on it. In the last section, we tried to summarize the performance analysis of the various proposed architectures. KeywordsAlgorithms, Protein Structure Prediction, Parallel Genetic Algorithms, Distributed Memory Architecture In cross over transformation, the individual pieces of solution are completely swapped to give new solutions and their fitness is tested upon for results. After finishing crossover, the concept of mutation is applied which involves random selection of new characteristics for the solution obtained. The termination is completely based on pre- defined number of steps or a pre-specified level of optimality for future reference as well. Genetic Algorithms significantly provides alternative methods for solving the problem and consistently performs other traditional methods efficiently without any errors. Most of the real world problems that involves finding of optimal parameter may seem to be difficult for traditional methods but are ideal for Genetic Algorithms in many cases. Although these are highly skilful in solving hard problems, they might also take longer execution times as compared to others. To counter this execution delay problem, scientists resorted to exploit the parallel nature of Genetic Algorithms. Thus, these came to be known as parallel genetic algorithms. Their implementation generally involves very similar issues as other parallel algorithms do which are explained in terms of granularity, synchronization and locality of reference etc. In