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# Genetic algorithms

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Presentation for CraftmansGuild on 15-Jan-2013. Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization.

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### Genetic algorithms

1. 1. Evolving Solutions Machine Learning Programs that Search for Solutions Searching Random Points Recursive Descent / Ascent Sub-Optimization Faulty Analogies Fitness Genetic Algorithms Particle Swarm Optimization Simulated AnnealingMonday, January 14, 13 1
2. 2. Basic ConceptsMonday, January 14, 13 2
3. 3. Learning ==> Improvement Some quality is being improved. There is some measure of good / bad. There is some way to move toward good. Change position Change behaviorMonday, January 14, 13 3
4. 4. Learning ==> Some quality is being improved. Implies / Assumes: There is consensus about good versus bad. There is some way to measure good / bad. Give that measure a name: “Fitness” Analogy to nature and evolution concept.Monday, January 14, 13 4
5. 5. Searching for Better Random Search Randomize values in degrees of freedom. Compare resulting ﬁtness. Pick the best results. And then what? Recursive Descent / Ascent Assumes that solution space has a gradient. Assumes there are minima / maxima.Monday, January 14, 13 5
6. 6. Seeker: When do you quit searching? Inﬁnite Loop versus “Stopping Condition” Eventually we run out of time / resource / energy. Complications: Sometimes “Best” is not well-deﬁned. The solution space is continuously changing. The shape (gradient qualities) of space is unknown.Monday, January 14, 13 6
7. 7. Faulty AnalogsMonday, January 14, 13 7
8. 8. Evolution There is some way to measure “ﬁtness” Fitness is a function of a set of objects that can vary over individual instances. Call the objects “genes” to use genetic analogy. Mutation ==> Changing the values of “genes”. Sexual Reproduction ==> Merging gene “sequences.” Next Generation <== keep most ﬁt, cull least ﬁt.Monday, January 14, 13 8
9. 9. Genetic Algorithm Deﬁne degrees of freedom ==> genes. Produce ﬁrst generation with randomized genes. Evaluate ﬁtness of each individual Calculate / Run a simulation of environment. Keep most ﬁt <==> Cull least ﬁt. Populate the next generation. ( mutate and/or splice ) Repeat until “done” Note: Real environments change over time.Monday, January 14, 13 9
10. 10. Main Loop In SmalltalkMonday, January 14, 13 10
11. 11. Next GenerationMonday, January 14, 13 11
12. 12. MutationMonday, January 14, 13 12
13. 13. Degrees Of FreedomMonday, January 14, 13 13