Genetic Algorithms, Search Algorithms. Jae C. Oh. Overview . Search Algorithms Learning Algorithms GA Example. Brief History. Evolutionary Programming Fogel in 1960s Individuals are encoded to be finite state machines Intellgent Behavior Evolutionary Strategies
Set of Hypothesis
From the Tree of the Life Website,University of Arizona
0 START : Create random population of n chromosomes
1 FITNESS : Evaluate fitness f(x) of each chromosome in the population
2 NEW POPULATION
0 SELECTION : Based on f(x)
1 RECOMBINATION: Cross-over chromosomes
2 MUTATION : Mutatechromosomes
3 ACCEPTATION : Reject or accept new one
3 REPLACE : Replace old with new population: the new
4 TEST : Test problem criterium
5 LOOP : Continue step 1 – 4 until criterium is satisfied
Genetic Algorithms (GAs)
Specialized algorithms – best performance for special problems
Genetic algorithms – good performance over a wide range of problems
They are fundamentally the same!!
1.235 5.323 0.454 2.321 2.454
1 5 3 2 6 4 7 9 8
(left), (back), (left), (right), (forward)
8 5 6 7 2 3 1 4 9Encoding Methods
Binary Encoding/Ternary Encoding
Permutation Encoding (TSP)
Real numbers, etc. Specialized
The process that determines which solutions are to be preserved and allowed to reproduce and which ones deserve to die out.
Chromosome # Fitness
Strings that are fitter are assigned a larger slot and hence have a better chance of appearing in the new population.
Minimum conflict fitness function.