Research Article Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture Ajay Shrestha and Ausif Mahmood Department of Computer Science and Engineering, University of Bridgeport, 126 Park Avenue, Bridgeport, CT 06604, USA Correspondence should be addressed to Ajay Shrestha; shrestha@my.bridgeport.edu Received 26 November 2015; Revised 13 March 2016; Accepted 21 March 2016 Academic Editor: Niansheng Tang Copyright © 2016 A. Shrestha and A. Mahmood. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Genetic Algorithm (GA) is a metaheuristic used in solving combinatorial optimization problems. Inspired by evolutionary biology, GA uses selection, crossover, and mutation operators to efciently traverse the solution search space. Tis paper proposes nature inspired fne-tuning to the crossover operator using the untapped idea of Mitochondrial DNA (mtDNA). mtDNA is a small subset of the overall DNA. It diferentiates itself by inheriting entirely from the female, while the rest of the DNA is inherited equally from both parents. Tis unique characteristic of mtDNA can be an efective mechanism to identify members with similar genes and restrict crossover between them. It can reduce the rate of dilution of diversity and result in delayed convergence. In addition, we scale the well-known Island Model, where instances of GA are run independently and population members exchanged periodically, to a Continental Model. In this model, multiple web services are executed with each web service running an island model. We applied the concept of mtDNA in solving Traveling Salesman Problem and to train Neural Network for function approximation. Our implementation tests show that leveraging these new concepts of mtDNA and Continental Model results in relative improvement of the optimization quality of GA. 1. Introduction Genetic Algorithm is a nature inspired metaheuristic used to solve optimization and search problems which would other- wise take a long time to solve using brute force methods. GA provides us the means to traverse the solution search space intelligently and to come up with a near optimal solution in a substantially short amount of time. Genetic Algorithms are used beyond computer science, engineering, and mathemat- ics, in areas such as economics, bioinformatics, life sciences, and manufacturing. GA is well suited for combinatorial opti- mization problems. One such problem where we can deploy GA is the Traveling Salesman Problem (TSP). Te goal of Genetic Algorithm is to come as close as possible to the optimal solution. Since the solution search space is so huge, the major difculty in reaching this goal is the convergence into local minima before exploring the entire search space for global minima. Tis is where we could exploit the concept of mtDNA to help add some order in the random search for near optimal solution. 2. Genetic Algorithm Te idea of GA was proposed by Holland in his 1975 book [1]. Since then GA has been an active feld of research and there has been numerous publications on it. TSP is one of the problems where GA has been successfully used. As shown in Figure 1, GA has two primary functions: pop- ulation selection and crossover. Selection algorithm describes the methodology to pick parents that will create children for the next generation. Tere are four strategies shown in the diagram: elite, roulette, rank, and tour. Te elite strategy gives preference to selecting the best members from the current population itself [2]. In roulette selection, members are mapped to a roulette wheel occupying space that is pro- portional to their ftness and members are selected randomly from it avoiding duplicates [3]. Rank selection method is similar to roulette, but instead of proportional representation of the pie based on ftness, members are ranked in ascending order based on their ftness [2]. In tournament selection,  population members are chosen to compete and the best one Hindawi Publishing Corporation Journal of Mathematics Volume 2016, Article ID 4015845, 10 pages http://dx.doi.org/10.1155/2016/4015845