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Posted May 22, 2012 by Team AnalyticpediA in Analytics
 
 

Genetic Algorithm Applications

Genetic Analyticpedia
Genetic Analyticpedia

Genetic Algorithm is a heuristic search technique used in computing and Artificial Intelligence to find optimized solutions to search problems using techniques inspired by evolutionary biology: mutation, selection, reproduction and recombination.

Nearly everyone can gain benefits from Genetic Algorithms, once he can encode solutions of a given problem to chromosomes in GA, and compare the relative performance (fitness) of solutions. An effective GA representation and meaningful fitness evaluation are the keys of the success in GA applications. The appeal of GAs comes from their simplicity and elegance as robust search algorithms as well as from their power to discover good solutions rapidly for difficult high-dimensional problems. GAs are useful and efficient when the search space is large, complex or poorly understood, Domain knowledge is scarce or expert knowledge is difficult to encode to narrow the search space, No mathematical analysis is available,Traditional search methods fail.

The advantage of the GA approach is the ease with which it can handle arbitrary kinds of constraints and objectives; all such things can be handled as weighted components of the fitness function, making it easy to adapt the GA scheduler to the particular requirements of a very wide range of possible overall objectives.

GAs have been used for problem-solving and for modeling. GAs are applied to many scientific, engineering problems, in business and entertainment such as :Automotive Design,Engineering Design, Optimized Telecommunications Routing, Trip, Traffic and Shipment Routing, Computer Gaming, Encryption and Code Breaking, Computer-Aided Molecular Design, Gene Expression Profiling, Optimizing Chemical Kinetic Analysis, Finance and Investment Strategies, Marketing and Merchandising


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