## Main.GeneticAlgorithms History

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!!!! Additional Information

[[http://boxcar2d.com | Box Car 2D Genetic Algorithm Example]]

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(:title Genetic Algorithms in Engineering Design:)

(:keywords genetic algorithms, continuous optimization, mathematical modeling, discrete optimization, nonlinear, optimization, engineering optimization, interior point, active set, differential, algebraic, modeling language, university course:)

(:description One often encounters problems in which design variables must be selected from among a set of discrete values:)

[[Attach:chap5_genetic_algorithms.pdf | Chapter 5: Genetic Algorithms]]

Gradient-based algorithms have some weaknesses relative to engineering optimization. Specifically, it is difficult to use gradient-based algorithms for optimization problems with:

# discrete-valued design variables

# large number of design variables

# multiple local minima, maxima, and saddle points

# nondifferentiable objectives and constraints

# analysis programs which crash for some designs

In recent years, a new family of optimization algorithms has emerged for dealing with the above characteristics. These algorithms are known as evolutionary algorithms. Evolutionary algorithms mimic the optimization process in nature as it optimizes biological species in order to maximize survival of the fittest. One type of evolutionary algorithm is the genetic algorithm. We will examine genetic algorithms in detail.

We express appreciation to Professor Richard J. Balling of the Civil and Environmental Engineering Department at BYU for allowing us to use this chapter.

(:keywords genetic algorithms, continuous optimization, mathematical modeling, discrete optimization, nonlinear, optimization, engineering optimization, interior point, active set, differential, algebraic, modeling language, university course:)

(:description One often encounters problems in which design variables must be selected from among a set of discrete values:)

[[Attach:chap5_genetic_algorithms.pdf | Chapter 5: Genetic Algorithms]]

Gradient-based algorithms have some weaknesses relative to engineering optimization. Specifically, it is difficult to use gradient-based algorithms for optimization problems with:

# discrete-valued design variables

# large number of design variables

# multiple local minima, maxima, and saddle points

# nondifferentiable objectives and constraints

# analysis programs which crash for some designs

In recent years, a new family of optimization algorithms has emerged for dealing with the above characteristics. These algorithms are known as evolutionary algorithms. Evolutionary algorithms mimic the optimization process in nature as it optimizes biological species in order to maximize survival of the fittest. One type of evolutionary algorithm is the genetic algorithm. We will examine genetic algorithms in detail.

We express appreciation to Professor Richard J. Balling of the Civil and Environmental Engineering Department at BYU for allowing us to use this chapter.