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Genetic Algorithms

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Powerpoint Slideshow on Genetic Algorithms presented to a class to fulfil the module EJJ 220.

Text of Genetic Algorithms

Genetic Algorithms

Genetic AlgorithmsModelling nature for engineering solutions

EJJ 210Fouche, GJ13004019

Genetic AlgorithmsIntroductionBasisAdvantagesHow the Algorithm WorksAlgorithm OperatorsReal world ApplicationsUse in Antenna DesignConclusion

Introduction What are GAs ?Genetic Algorithms are a Darwinian natural-selection based search heuristic used to evolve solutions to abstracted problems.

First tested in 1954

Basis of Genetic AlgorithmsBased on Darwin's principle of Natural selectionThe idea of Survival of the FittestDevelopment of advantageous attributes through survival over generationsThe use of operators like Genetic crossover, mutation and fitness evaluation to evolve a robust solution to the problemInformation on the solution is represented as a line of Genes in a ChromosomeEasy to code in an object orientated language

Advantages of GAs ?Good at navigating huge search spaces, looking for optimal combinations to form a solution to the task at hand.Particularly useful when the solution cannot be derived mathematicallyBecause solutions are abstracted, genetic algorithms are applicable to many areas of engineering, intelligent and complex systems.Solutions rated and evolved according to an objective and variable fitness criteriaLimited only by computational power.Their strongest attribute is being based on a system that already works so perfectly nature.

Algorithm Operators - ChromosomeThe Chromosome is the way that information is represented, and it contains the steps of the solution.In G.A. Chromosomes are simply long arrays of Genes that encode informationEach Gene represents an encoded step, or characteristic. Often a binary representation.

Algorithm Operators & Fitness EvaluationA fitness function is simply an objective measure of a Chromosomes fitness with regard to the problem at hand.The general idea is to give a higher fitness score the closer a chromosome comes to solving the problem.

8Applications of Genetic AlgorithmsStock TradingControl System debuggingEvolving Medicines (IcoSystem)Airline route optimizationAntenna designEfficient wind turbine designThe training of Artificial Intelligence networksMany more...

Use in Antenna DesignIn the past few years, Genetic Algorithms have been used to evolve antenna designs primarily with unusual design requirements, such as unusual radiation patterns.Example: Antennae on the NASA ST5 satellitesIts unusual a-symmetric design would have been impossible to design using manual methods.

Conclusion