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This course is about robots intelligence. This lecture is one of many lectures on robots you can find in my uploads. Following key points are hint to specific topics of this lecture. Fast Evolutionary, Claims, Simulated, Significantly Better Scalability, Evolutionary Time, Number of Iterations, Genetic Algorithm, Population, Individuals, Generation
Typology: Slides
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Survival of the
Fittest Reproduction
with Crossover
15
60
20 48
1-Bit
Mutate
Score
Chromosome
Mutated
Chromosome
Evaluate
If Better
If Worse
Probability
Test
You still keep
worse
solutions with
some
probability
ocsity.co
Convergence
αααα
= MaxMutationRate = UserFraction x ChromosomeBits
Convergence
Acceptance
Probability
0.
Acceptance
Probability
0.
Acceptance
Probability
0.
A
B
ρ
= MaxSearchProbability = UserFraction
Iterations Solving Binary F
99% Complete
100% Complete
x
σ
σ σ
σ
x
σσσσ
Genetic
Algorithm
Annealing Simulated
HereBoy
Easy and Hard Binary F6 Experiments
HereBoy
Simulated Annealing
Genetic
Algorithm
Scale Between
Number of
iterations
ocsity.co
Iteration = 0 Score = 800
Iteration = 3,
Score = 1,
Iteration = 65,
Score = 1,
Pattern Generator Circuit Statistics
10x
20x
Search Space Size
6400
25,
Probability of
Randomly Creating
100% Solution
HereBoy
Simulated Annealing
Genetic
Algorithm
Maximum Score