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

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.

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

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 110010 has fitness 1, .. 101011 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.