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

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

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

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