18-07-2012, 01:11 PM
Operation of Genetic Algorithms
Data_mining_GA.ppt (Size: 650.5 KB / Downloads: 25)
Fitness Function
A fitness function quantifies the optimality of a solution (chromosome) so that that particular solution may be ranked against all the other solutions
It depicts the closeness of a given ‘solution’ to the desired result.
Watch out for its speed.
Most functions are stochastic and designed so that a small proportion of less fit solutions are selected. This helps keep the diversity of the population large, preventing premature convergence on poor solutions.
Tournament Selection
Runs a "tournament" among a few individuals chosen at random from the population and selects the winner (the one with the best fitness) for crossover
Two entities are picked out of the pool, their fitness is compared, and the better is permitted to reproduce.
Selection pressure can be easily adjusted by changing the tournament size.
Deterministic tournament selection selects the best individual in each tournament.
Independent of Fitness function.
Rank Selection
Rank selection first ranks the population and then every chromosome receives fitness from this ranking.
Selection is based on this ranking rather than absolute differences in fitness.
The worst will have fitness 1, second worst 2 etc. and the best will have fitness N (number of chromosomes in population).
Crossover
Single Point Crossover- A crossover point on the parent organism string is selected. All data beyond that point in the organism string is swapped between the two parent organisms.
Characterized by Positional Bias