This document discusses genetic algorithms and their applications. It provides an overview of genetic algorithms, including the main steps and operators involved like selection, crossover and mutation. It then discusses several applications of genetic algorithms, such as automotive and engineering design, robotics, optimized telecommunications routing, evolvable hardware, and computer gaming.

2.  Genetic algorithm
 Introduction
 Software package
 Data structure
 Fitness assignment
 Selection function
 Mutation operators
 Automotive design
 Engineering design
 Robotics
 Optimized
telecommunications
routing
 Evolvable hardware
 Computer gaming

3.  Genetic Algorithm (GA) is a calculus free
optimization technique based on principles of natural
selection for reproduction and various evolutionary
operations such as crossover, and mutation. Various
steps involved in carrying out optimization through
GA are described. Three applications, viz. finding
maximum of a mathematical function, obtaining
estimates for a multiple linear regression model, and
fitting a nonlinear statistical model through GA
procedure, are discussed. Finally, results are
compared to those obtained from standard (calculus
based) solution techniques.

4.  A genetic algorithm (GA) is a search and optimization
method which works by mimicking the evolutionary
principles and chromosomal processing in natural
genetics. A GA begins its search with a random set of
solutions usually coded in binary strings. Every solution
is assigned a fitness which is directly related to the
objective function of the search and optimization
problem. Thereafter, the population of solutions is
modified to a new population by applying three operators
similar to natural genetic operators reproduction,
crossover, and mutation. It works iteratively by
successively applying these three operators in each
generation till a termination criterion is satisfied. Over
the past decade and more, GAs have been successfully
applied to a wide variety of problems, because of their
simplicity, global perspective, and inherent parallel
processing.

5.  Whilst there exist many good public-domain genetic
algorithm packages, such as GENESYS and
GENITOR, none of these provide an environment
that is immediately compatible with existing tools in
the control domain. The MATLAB Genetic
Algorithm Toolbox aims to make GAs easily
accessible. This allows the retention of existing
modeling and simulation tools for building objective
functions and allows the user to make direct
comparisons between genetic methods and
traditional procedures.

6.  The main data structures in the GA Toolbox are
chromosomes, phenotypes, objective function values
and fitness values. The chromosome structure stores
an entire population in a single matrix of size Nind ×
Lind, where Nind is the number of individuals and
Lind is the length of the chromosome structure.
Phenotypes are stored in a matrix of dimensions
Nind × Nvar where Nvar is the number of decision
variables. An Nind × Nobj matrix stores the
objective function values, where Nobj is the number
of objectives. Finally, the fitness values are stored in
a vector of length Mind. In all of these data
structures, each row corresponds to a particular
individual.

7.  The fitness function transforms the raw objective
function values into non-negative figures of merit
for each individual. The Toolbox supports the
offsetting and scaling method and the linear-
ranking algorithm. In addition, non-linear
ranking is also supported in the routine ranking.

8.  These functions select a given number of individuals
from the current population, according to their
fitness, and return a column vector to their indices.
Currently available routines are roulette wheel
selection, rows, and stochastic universal sampling,
sues. A high-level entry function, select, is also
provided as a convenient interface to the selection
routines, particularly where multiple populations are
used. In cases where a generation gap is required, i.e.
where the entire population is not reproduced in each
generation, reins can be used to effect uniform
random or fitness-based re-insertion.

9.  Binary and integer mutation are performed by the
routine mutt. Real-value mutation is available
using the breeder GA mutation function,
mudbug. Again, a high-level entry function,
mutate, to the mutation operators is provided.

10.  Using Genetic Algorithms [GAs] to both design
composite materials and aerodynamic shapes
for and regular means of transportation (including
aviation) can return combinations of best materials
and best engineering to provide faster, lighter, more
fuel efficient and safer vehicles for all the things we
use vehicles for. Rather than spending years in
laboratories working with polymers, wind tunnels
and balsa wood shapes, the processes can be done
much quicker and more efficiently by computer
modeling using GA searches to return a range of
options human designers can then put together
however they please.

11.  Getting the most out of a range of materials to
optimize the structural and operational design of
buildings, factories, machines, etc. is a rapidly
expanding application of GAs. These are being
created for such uses as optimizing the design of heat
exchangers, robot gripping arms, satellite booms,
building trusses, flywheels, turbines, and just about
any other computer-assisted engineering design
application. There is work to combine GAs
optimizing particular aspects of engineering
problems to work together, and some of these can
not only solve design problems, but also project
them forward to analyze weaknesses and possible
point failures in the future so these can be avoided.

12.  Robotics involves human designers and engineers trying
out all sorts of things in order to create useful machines
that can do work for humans. Each robot’s design is
dependent on the job or jobs it is intended to do, so there
are many different designs out there. GAs can be
programmed to search for a range of optimal designs and
components for each specific use, or to return results for
entirely new types of robots that can perform multiple
tasks and have more general application. GA-designed
robotics just might get us those nifty multi-purpose,
learning robots we’ve been expecting any year now since
we watched the Jet sons as kids, who will cook our
meals, do our laundry and even clean the bathroom for
us!

13.  Do you find yourself frustrated by slow LAN
performance, inconsistent internet access, a FAX
machine that only sends faxes sometimes, your land
line’s number of ‘ghost’ phone calls every month? Well,
GAs are being developed that will allow for dynamic and
anticipatory routing of circuits for These could take
notice of your system’s instability and anticipate your re-
routing needs. Using more than one GA circuit-search at
a time, soon your interpersonal communications
problems may really be all in your head rather than in
your telecommunications system. Other GAs are being
developed to optimize placement and routing of cell
towers for best coverage and ease of switching, so your
cell phone and blackberry will be thankful for GAs too.

14.  applications are electronic circuits created by GA
computer models that use stochastic (statistically
random) operators to evolve new configurations
from old ones. As the algorithm does its thing in the
running model, eventually a circuit configuration
will come along that does what the designer wants.
Think of reconfigurable circuits in something like a
space robot. It could use a built-in GA library and
simulator to re-design itself after something like
radiation exposure that messes up its normal
configuration, or encounters a novel situation in
which it needs a function it doesn’t already have.
Such GAs would enable self-adaptation and self-
repair

15.  Those who spend some of their time playing
computer Sims games (creating their own
civilizations and evolving them) will often find
themselves playing against sophisticated artificial
intelligence GAs instead of against other human
players online. These GAs have been programmed to
incorporate the most successful strategies from
previous games – the programs ‘learn’ – and usually
incorporate data derived from game theory in their
design. Game theory is useful in most all GA
applications for seeking solutions to whatever
problems they are applied to, even if the application
really is a game.