Lecture_7_Genetic_algorithms (1).pdf

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   Approx. Models physicalmathematicalderivative vs function based ã Taylor series based, ã response surfaces ã Neural Networks  Analysis for design cost vs. accuracy by avoiding expensive reanalysis Optimisation algorithms math programimgstochastic ã linear, ã nonlinear, ã discrete ã random, ã simulated annealing, ã genetic algorithm Optimisation procedure all-in-onede-compositionwith & without coordination ã Hierarchical ã non-hierarchical, ã hybrid Human interface ã formulation, ã interpretation, ã coordination, ã distributed shared computing, ã web-based design ã decoupling of multi-disciplinary modules, ã using sensitivity analysis Genetic Algorithms (GAs)  Contents ã Optimisation methods revisitied ã Soft computing tools ã Introduction to Genetic Algorithms ã Coding/representation of design variables ã Mechanisms of Genetic Search ã Fitness definition and selection of members ã Basic Operations ã Schema Theorem ã Constraint Handling ã Expression based Strategies ã Real coded Genetic Algorithms  Optimisation methods revisited ▪ Enumerative schemes  Evaluate objective function at a number of points in the design space. Most practical problems are not amenable to this approach ▪ Random search  random-walk search techniques are like enumerative schemes ▪ Mathematical programming  Efficient method for restrictive class of problems. Requires continuity and unimodality of the design space ▪ Soft computing tools bridge this gap ▪ Genetic algorithm is the most popular amongst the soft computing tools  Soft Computing tools ▪ Soft computing tools are supposed to emulate the human brain. The tools are based on partial truth and are approximate ▪ Soft computing tools tolerate uncertainty and can be less precise compared conventional computational methods (hard computing) ▪ Some soft computing tools are  Genetic algorithms (GAs)  Simulated annealing (SA)  Neural networks  Immune network modelling  Fuzzy logic  Machine learning paradigms
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