Optimization technique genetic algorithm

Optimization technique genetic algorithm

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  Optimization Technique -Genetic Algorithm
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  OPTIMIZATION  It’s aprocedure 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.
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  GENETIC ALGORITHM  Agenetic 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.
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   HISTORY  Basedon 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.
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  Biological Evolution: Organisms producea number of offspring similar to themselves but can have variations due to: –Mutations(random changes)
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  Some offspring survive,and produce next generations, and some don’t:
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  G A PROCEDURE Atypical genetic algorithm requires two things to be defined:  a genetic representation of the solution domain.  a fitness function to evaluate the solution domain.
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  PROBLEM DOMAINS  Problemswhich 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.
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  What Do WeMean 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.
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  It Is ASearch Technique
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  Applications :  Automateddesign 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
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  Application : continue. Software engineering.  Traveling Salesman Problem.  Mobile communications infrastructure optimization.  Electronic circuit design, known as Evolvable hardware.
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  Genetic Algorithm Presenting GenerationCycle
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  - As with thehuman 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.
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  Advantages : A GAhas 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.
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  Disadvantages :  Apractical 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.
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  Conclusion : Evolutionary algorithms havebeen 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.
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  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.
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  Thanking you