Asee peer logo

Genetic Algorithms: Theory And Application

Download Paper |

Conference

1998 Annual Conference

Location

Seattle, Washington

Publication Date

June 28, 1998

Start Date

June 28, 1998

End Date

July 1, 1998

ISSN

2153-5965

Page Count

6

Page Numbers

3.298.1 - 3.298.6

Permanent URL

https://peer.asee.org/7144

Download Count

17

Request a correction

Paper Authors

author page

Edgar N. Reyes

author page

Dennis I. Merino

author page

Carl W. Steidley

Download Paper |

Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Session 2220

Genetic Algorithms: Theory and Application

Dennis I. Merino, Edgar N. Reyes/Carl Steidley Southeastern Louisiana University/Texas A&M University - Corpus Christi Hammond, LA 70402/ Corpus Christi, TX 78412

1 Introduction

Genetic algorithms, a class of robust and efficient search techniques that can be randomly sample large spaces, have applications in the field of optimization and in a wide range of computer science problems in pattern recognition, search, scheduling, and machine learning. Genetic algorithms are motivated by characteristics found in natural population genetics, among them robustness and efficiency. Features of biological systems found in genetic algorithms include reproduction, self- guidance, self-repair, the nature of survival of the fittest, and variation through mutation. Genetic algorithms were developed by John Holland of the University of Michigan in the 1970's. Many of the essential properties of genetic algorithms discussed in this paper can be found in [1, 2].

When a genetic algorithm is used to find an optimal solution in the space of all feasible solutions, the algorithm maintains a population (or set) of feasible solutions which evolve through random process based on principles found in the mechanics of natural selection and genetics. Each time this set of solution evolves (or as we say iterated), a new set of solutions is generated. The iterations are repeated several times as necessary. An important goal from the standpoint of genetic algorithms is to improve the values of the objective function in an efficient, practical, or quick method. Ideally, as it is true for many optimization techniques, one would like to generate after several iterations a set of solutions that includes an optimal global solution; but a genetic algorithm has its priority set on tying improvement of an objective function with performance-practicality of the method. In the literature, one finds several applications of genetic algorithms in routing and scheduling problems, machine learning, setting weights in neural nets, and testing expert systems that control complex systems, amongst others [2]. In the next section, we focus on the algorithm itself by solving an elementary optimization problem.

2 Genetic Algorithm: An example

We present the following to illustrate the effectiveness of genetic algorithms. We note that although the problem can be solved easily using a different method, it is the process involved with genetic algorithms that is put to focus.

We wish to

Reyes, E. N., & Merino, D. I., & Steidley, C. W. (1998, June), Genetic Algorithms: Theory And Application Paper presented at 1998 Annual Conference, Seattle, Washington. https://peer.asee.org/7144

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 1998 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015