# Set Based Search Modeling Examples II

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Set Based Search Modeling Examples II
Andrew Kuipers Please include [CPSC433] in the subject line of any s regarding this course. CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack 0-1 Knapsack Problem For a given list of n items: with weights W = <w1, …, wn> and values V = <v1, …, vn> we want to maximize the value of a knapsack with capacity C by either placing, or not placing, items from I into C CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack Facts The items in our knapsack, which we can simply represent by their index F = { 1, …, n } States A state is a set of items, where the sum of the weight of the items is less than or equal to capacity C S = { F’  F | fF’ wf  C } CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack Extension Rules Ext = { A  B | sS  AS | ((s-A)  B)  S } This basic extension rule definition allows us to perform all sorts of set manipulation, so long as the result is a valid state ie: adding and removing either single or multiple items from the knapsack, as long as the result weighs less than the maximum capacity C CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack Example: W = { 20, 7, 13, 5 } V = { 50, 30, 25, 15 } C = 25 s0 = { } s1 = { 2, 3 } by A = { }  B = { 2, 3 } s2 = { 2, 3, 4 } by A = { }  B = { 4 } s3 = { 1, 4 } by A = { 2, 3 }  B = { 1 } CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack Problems with this Model The control must select between a large number of possible extension rules to apply Only a single possible solution being manipulated: how can we compare solutions? How might we define the goal? While we have correctly modeled the problem, this model does not lend well to a search process Remember: for a given problem, there are many ways to construct a model CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA Another approach: Genetic Algorithm Brief Definition: A genetic algorithm (GA) is a search model that mimics the process of natural evolution. A population of potential solutions (individuals) Mutate and combine to create new individuals Use a fitness function to evaluate individuals CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA GA Operators Brief Introduction Mutation Crossover CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA Genetic Algorithm Approach I = (w1, v1), …, (wn, vn), C = capacity where w((wi, vi)) = wi and v((wi, vi)) = vi Facts F = { { i1, …, im } | 1  j  m ij  I  (j=1..m w(ij))  C } CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA States S  2F * do we need to get any more specific? Extension Rules Ext = { A → B | sS  AS | (s-A)BS  (Mutation(A,B)  Combination(A,B) } CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA Mutation(A,B)  A = { P }  B = { P, (P – K)  J } where K  P , J  I and (K  J) =  Remove some subset K from the fact P randomly Replace it with some set of items J that does not contain any items in K We don’t want to replace the fact, just create a new fact that is a mutation of original fact. CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA Mutation Example: I = (3, 4), (9, 7), (7, 3), (6, 9), (11,8)  C = 25 A = { (3,4), (7,3), (6,9) } = P K = { (3,4), (6,9) }  P J = { (9,7), (11,8) }  I (K  J) =  CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA Mutation Example: I = (3, 4), (9, 7), (7, 3), (6, 9), (11,8)  C = 25 A = { (3,4), (7,3), (6,9) } = P K = { (3,4), (6,9) }  P J = { (9,7), (11,8) }  I (K  J) =  B = { P, (P – K)  J } = { P, { (7,3), (9,7), (11,8) } } CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA Combination(A,B)  A = { P, Q }  B = { P, Q, K } where: K  (P  Q)  (K  P)  (K  Q)  min(|P|,|Q|)  |K|  max(|P|, |Q|) Use existing facts P and Q to generate a new fact K, which is a combination of P & Q, yet not equal to P or Q, and of size between that of P and Q CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA Combination Example: I = (3, 4), (9, 7), (7, 3), (6, 9), (11,8)  C = 25 P = { i1, i3 } Q = { i2, i3, i4 } P  Q = {i1, i2, i3, i4 } K = { i2, i3 }  (P  Q) CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA Combination Example: I = (3, 4), (9, 7), (7, 3), (6, 9), (11,8)  C = 25 P = { i1, i3 } Q = { i2, i3, i4 } P  Q = {i1, i2, i3, i4 } K = { i2, i3 }  (P  Q) B = {P, Q, { i2, i3 } } CPSC 433 Artificial Intelligence

CPSC 433 Artificial Intelligence
Example: 0-1 Knapsack GA How does search proceed in a GA? What does a search instance look like? Should a search control only select the best individuals in the state? Population control, genocide CPSC 433 Artificial Intelligence

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