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Optimal Procedure for Variable Selection Using Genetic Algorithm

Explore an optimal procedure for variable selection and QSAR model building using Genetic Algorithm. Understand the concept of schemata theorem in Holland's genetic algorithms and learn about fitness evaluations, crossover, mutation, and selection strategies. Follow a step-by-step process to ensure the best individuals survive and evolve. Source: A. Yasri and D. Hartsough, Toward an Optimal Procedure for Variable Selection and QSAR Model Building, J. Chem. Inf. Comput. Sci. 2001.

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Optimal Procedure for Variable Selection Using Genetic Algorithm

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  1. SCHEMATA THEOREM (Holland) • h(i) raw fitness for population sample i • f(i) = normalized fitness f(i) = h(i)/Σh(i) • A schema denotes a set of substrings that have identical • values at certain loci: 1#101 = {10101, 11101} • m(S,t) number of scheme exemplars in pop at generation t • Number of schema of individual S present in next generation is • proportional to chance of an individual being picked that has • the schema according to: • m(S,t+1) = m(S,t) n f(S)/Σf = m(S,t) f(S)/fave= m(S,t) fave(1+c) • m(S,t+1) = m(S,0) (1+c)t • Better than average schemata grow exponentially

  2. Partially Mapped Crossover

  3. Initial Population Evaluation Fitness proportional Crossover Parents Tournament Selection Selected Population Mutation Rank selection Parents Offspring Elitist strategy Evaluation Next Generation Make sure that best individual survives Genetic Algorithm cycle

  4. Note: In the plot, fitnesses are plotted as (1-R2) and The problem can be thought as a minimization.

  5. Source: A. Yasri andD. Hartsough, Toward an Optimal Procedure for Variable Selection and QSAR Model Building J. Chem. Inf. Comput. Sci. 2001 Vol. 41, No.5, pp. 1218-1227.

  6. Search space in feature selection A data set with 10 features

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