1. An evolutionary approach to solving complex problems
Genetic Algorithms
An evolutionary approach to solving complex problems

2. Genetic Algorithms - Overview
The genetic algorithm can be applied to complex problems as follows:
Stage 1 : Individual potential solutions are encoded into representations that support variation and selection operations
Stage 2 : Mating and mutation algorithms produce a new generation of individuals that recombine features of their parents
Stage 3 : Individual (solutions) are ranked as to their proximity to a final solution and higher ranked individuals are favored for selection to reproduce.

3. Genetic Algorithm Pseudocode
Let P(t) = {x1t, x2t , . . xnt } be population at time t
Procedure GA
Begin
Set time t = 0
Initialize P(t)
While not terminate
Evaluate fitness of each population member P(t)
Select members of P(t) based on fitness
Produce offspring of these pairs using genetic operators of crossover and mutation
Replace individuals of P(t) based on fitness with these offspring
Increment time t
End

4. Conjunctive Normal Form Satisfiability
(A  C)  (A  C   E)  (B  C  D   E)  (A   B  C)  ( E  F)
Genetic Representation
Since there are 6 literals A..E, population members consist of bit strings of 6 bits
Fitness function measures how many of the 5 clauses bit string makes true
has fitness 1, has fitness 5 (solution)
Reproduction is based on fitness
Crossover might combine 1st half of 1st member with 2nd half of second member for one offspring, and 2nd half of 1st member with 1st half of 2nd member for other offspring.