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Optimization Technique
-Genetic Algorithm
OPTIMIZATION
 It’s a procedure to make a system or
design as effective, especially involving the
mathematical techniques.
 To minimize the cost of production or to
maximize the efficiency of production.
GENETIC ALGORITHM
 A genetic algorithm (or short GA) is a
search technique used in computing to
find true or approximate solutions to
optimization and search problems.
 Genetic algorithms are categorized as
global search heuristics.
 Genetic algorithms are a particular class
of evolutionary algorithms.
 HISTORY
 Based on the mechanics of biological
evolution
 Initially developed by John Holland,
University of Michigan (1970’s)
 These algorithms are now used by a
majority of Fortune 500 companies to
solve difficult scheduling, data fitting,
trend spotting and budgeting problems,
and virtually any other type of
combinatorial optimization problem.
Biological Evolution:
Organisms produce a number of offspring
similar to themselves but can have variations
due to:
–Mutations(random changes)
Some offspring survive, and
produce next generations, and
some don’t:
G A PROCEDURE
A typical genetic algorithm requires two
things to be defined:
 a genetic representation of the solution
domain.
 a fitness function to evaluate the solution
domain.
PROBLEM DOMAINS
 Problems which appear to be particularly
appropriate for solution by genetic
algorithms include timetabling and
scheduling problems, and many scheduling
software packages are based on GAs. GAs
have also been applied to engineering
Genetic algorithms are often applied as an
approach to solve global optimization
problems.
 As a general rule of thumb genetic
algorithms might be useful in problem
domains that have a complex fitness
landscape as recombination is designed to
move the population away from local optima
that a traditional hill climbing algorithm might
get stuck in.
What Do We Mean By Genetic
Algorithm?
 It is started with a set of randomly
generated solutions and recombine pairs
of them at random to produce offspring.
 Only the best offspring and parents are
kept to produce the next generation.
It Is A Search Technique
Applications :
 Automated design of mechatronic
systems using bond graphs and genetic
programming (NSF).
 Code-breaking, using the GA to search
large solution spaces of ciphers for the
one correct decryption.
 Design of water distribution systems.
 Distributed computer network
topologies.
 Electronic circuit design, known as
Application : continue.
 Software engineering.
 Traveling Salesman Problem.
 Mobile communications infrastructure
optimization.
 Electronic circuit design, known as
Evolvable hardware.
Genetic Algorithm Presenting
Generation Cycle
-
As with the human race,
the weakest candidates
are eliminated from the
gene pool, and each
successive generation of
individuals contains
stronger and stronger
characteristics. It’s
survival of the fittest, and
the unique processes of
crossover and mutation
conspire to keep the
species as strong as
possible.
Advantages :
A GA has a number of advantages.
 It can quickly scan a vast solution set.
 Bad proposals do not effect the end
solution negatively as they are simply
discarded.
 The inductive nature of the GA means that it
doesn't have to know any rules of the
problem - it works by its own internal rules.
 This is very useful for complex or loosely
defined problems.
Disadvantages :
 A practical disadvantage of the genetic
algorithm involves longer running times
on the computer. Fortunately, this
disadvantage continues to be minimized
by the ever-increasing processing speeds
of today's computers.
Conclusion
:
Evolutionary algorithms have been around since
the early sixties. They apply the rules of nature:
evolution through selection of the fittest
individuals, the individuals representing solutions
to a mathematical problem.
Genetic algorithms are so far generally the best
and most robust kind of evolutionary algorithms.
References:
 A.D. Channon, and R.I. Damper, "Towards the
Evolutionary Emergence of Increasingly Complex
Advantageous Behaviours". International Journal of
Systems Science, 31(7), pp. 843-860, 2000.
 C.A. Balanis, Antenna Theory Analysis and Design
John Wiley & Sons, 2nd ed., 1997.
 Chakraborty .R .C, Fundamentals of Genetic
Algorithms, AI Course Lecture 39-40, June 01,2010.
Thanking
you

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Optimization technique genetic algorithm

  • 2. OPTIMIZATION  It’s a procedure to make a system or design as effective, especially involving the mathematical techniques.  To minimize the cost of production or to maximize the efficiency of production.
  • 3. GENETIC ALGORITHM  A genetic algorithm (or short GA) is a search technique used in computing to find true or approximate solutions to optimization and search problems.  Genetic algorithms are categorized as global search heuristics.  Genetic algorithms are a particular class of evolutionary algorithms.
  • 4.  HISTORY  Based on the mechanics of biological evolution  Initially developed by John Holland, University of Michigan (1970’s)  These algorithms are now used by a majority of Fortune 500 companies to solve difficult scheduling, data fitting, trend spotting and budgeting problems, and virtually any other type of combinatorial optimization problem.
  • 5. Biological Evolution: Organisms produce a number of offspring similar to themselves but can have variations due to: –Mutations(random changes)
  • 6. Some offspring survive, and produce next generations, and some don’t:
  • 7. G A PROCEDURE A typical genetic algorithm requires two things to be defined:  a genetic representation of the solution domain.  a fitness function to evaluate the solution domain.
  • 8. PROBLEM DOMAINS  Problems which appear to be particularly appropriate for solution by genetic algorithms include timetabling and scheduling problems, and many scheduling software packages are based on GAs. GAs have also been applied to engineering Genetic algorithms are often applied as an approach to solve global optimization problems.  As a general rule of thumb genetic algorithms might be useful in problem domains that have a complex fitness landscape as recombination is designed to move the population away from local optima that a traditional hill climbing algorithm might get stuck in.
  • 9. What Do We Mean By Genetic Algorithm?  It is started with a set of randomly generated solutions and recombine pairs of them at random to produce offspring.  Only the best offspring and parents are kept to produce the next generation.
  • 10. It Is A Search Technique
  • 11. Applications :  Automated design of mechatronic systems using bond graphs and genetic programming (NSF).  Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption.  Design of water distribution systems.  Distributed computer network topologies.  Electronic circuit design, known as
  • 12. Application : continue.  Software engineering.  Traveling Salesman Problem.  Mobile communications infrastructure optimization.  Electronic circuit design, known as Evolvable hardware.
  • 14. - As with the human race, the weakest candidates are eliminated from the gene pool, and each successive generation of individuals contains stronger and stronger characteristics. It’s survival of the fittest, and the unique processes of crossover and mutation conspire to keep the species as strong as possible.
  • 15. Advantages : A GA has a number of advantages.  It can quickly scan a vast solution set.  Bad proposals do not effect the end solution negatively as they are simply discarded.  The inductive nature of the GA means that it doesn't have to know any rules of the problem - it works by its own internal rules.  This is very useful for complex or loosely defined problems.
  • 16. Disadvantages :  A practical disadvantage of the genetic algorithm involves longer running times on the computer. Fortunately, this disadvantage continues to be minimized by the ever-increasing processing speeds of today's computers.
  • 17. Conclusion : Evolutionary algorithms have been around since the early sixties. They apply the rules of nature: evolution through selection of the fittest individuals, the individuals representing solutions to a mathematical problem. Genetic algorithms are so far generally the best and most robust kind of evolutionary algorithms.
  • 18. References:  A.D. Channon, and R.I. Damper, "Towards the Evolutionary Emergence of Increasingly Complex Advantageous Behaviours". International Journal of Systems Science, 31(7), pp. 843-860, 2000.  C.A. Balanis, Antenna Theory Analysis and Design John Wiley & Sons, 2nd ed., 1997.  Chakraborty .R .C, Fundamentals of Genetic Algorithms, AI Course Lecture 39-40, June 01,2010.

Editor's Notes