Local Beam search and Genetic algorithm in A.I

1. LOCAL search ALGORITHMS
14
Local Beam Search
Genetic Algorithm

2. Local Beam Search
 Unlike Hill-climbing, local beam search keep
track of k states rather than just one.
 It starts with k randomly generated states.
 At each step, all the successors of all the
states are generated.
 If any one is a goal, the algorithm halts,
otherwise it selects the k best successors
from the complete list and repeats.
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A
j K E F H
B D
C
C
K=2
14 13
15
8
9
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Limitation:
 It only explore best ‘k’ nodes that mean lack of diversity to remove this
problem Stochastic Beam search came into picture.
 Instead of choosing the best k from the pool of candidate successors, stochastic
beam search chooses k successors at random, with the probability of choosing
a given successor being an increasing function of its value.

4. Genetic Algorithm
 A genetic algorithm (or GA) is a variant of stochastic
beam search in which successor states are generated
by combining two parent states rather than by
modifying a single state.
 A genetic algorithm is a search heuristic that is
inspired by theory of biological evolution.
 This algorithm reflects the process of natural
selection where the fittest individuals are selected
for reproduction in order to produce offspring of
the next generation.
 It is frequently used to find optimal or near-
optimal solutions to difficult problems.
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5. Initial Population
Fitness Function
Selection
Crossover
Mutation
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6. 0 0 1 1 1 0 0 0
1 1 1 0 0 0 1 1
Crossover point
0 0 1 1 0 0 1 1
1 1 1 0 1 0 0 0
Parent 1
Parent 2
Offspring
Mutation
0 0 1 1 1 0 0 0 0 0 0 1 1 0 1 0
Parent 1 Offspring
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8. Thanks For
Watching
Reference:
Artificial Intelligence
A Modern Approach Third Edition
Peter Norvig and Stuart J. Russell
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