Date of Award

Spring 2010

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical Engineering

Committee Director

Ravindra P. Joshi

Committee Member

Frederic D. McKenzie

Committee Member

Praveen Sankaran

Call Number for Print

Special Collections LD4331.E55 G837 2010

Abstract

Genetic Algorithms find several applications in a variety of fields, such as engineering, management, finance, chemistry, scheduling, data mining and so on, where optimization plays a key role. This technique represents a numerical optimization technique that is modeled after the natural process of selection based on the Darwinian principle of evolution. The Genetic Algorithm (GA) is one among several optimization techniques and attempts to obtain the desired solution by generating a set of possible candidate solutions or populations. These populations are then compared and the best solutions from the set are retained. Subsequently, new candidate solutions are produced, and the process continues until the best solution subject to simulation time constraints or a set degree of convergence is met. Along the process of determining the optimized solution, the Genetic Algorithm technique implements various operations such as reproduction, selection, crossover, and mutation. Some important and relevant applications of genetic algorithms include determining the shortest route, lowest costs, highest returns etc. in systems defined by multi-variable parameters.

This thesis work mainly focuses on finding the shortest distance given the start and end-points, and a set of possible constraints. These constraints have been taken to be "forbidden" zones through which entry or passage is not allowed, and thus represents a "blocked route." A GA based technique to solve the above problem of finding the shortest route is proposed. Various GA techniques (selection, crossover, mutation) and their advantages, drawbacks and applications are discussed. The selection operator determines the suitability of the characteristics of future members of the population (or offspring) to that of the existing (or parent) members. Reproduction is used to obtain a solution that has the direct characteristics from the parents. Crossover is used to get a solution that has a combination of characteristics from both the parents based on certain criteria. Mutation is implemented in situations where diversity is desired to avoid convergence to a local minimum in the search space. In a deterministic approach, the shortest route is calculated by finding the neighbor points along the direction of the path. A discussion on the comparison of results obtained from both the techniques is done. It has been shown that the GA scheme is more efficient and would provide a better solution than the deterministic approach.

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DOI

10.25777/6r57-5589

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