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Immune Genetic Algorithms
By Jeremy Moreau
References
• Licheng Jiao, Senior Member, IEEE, and
Lei Wang, “A Novel Genetic Algorithm
Based on Immunity,” IEEE Transactions
on Systems, Man, AND Cybernetics—Part
A: Systems and Humans, Vol. 30, No. 5,
September 2000
Outline
• Introduction
• Immune genetic algorithm (IGA)
– Vaccination
– Immune Selection
• The immune operator
• Simulations
• Conclusions
Introduction
• All genetic algorithms use the mutation
and crossover operators
• This gives individuals the chance to evolve
into a more fit individual
• If target is difficult to reach, crossover and
mutation may introduce degeneracy into
generations of individuals
• Immunity can be introduced to help
prevent degeneration
The Immune Genetic Algorithm
(IGA)
• Uses local information to intervene in the
global process of mutation and crossover
• Curtails the degenerative phenomena from
arising during the evolution process
• Consists of two basic steps:
– The vaccination
– The immune selection
The Vaccination
• Given an individual, vaccination means
modifying the bits of some genes using
prior knowledge
• Satisfies two conditions:
– If each gene bit of an individual y is wrong,
the probability of transforming to y is 0
– If each gene bit of an individual y is optimal,
the probability of transforming to y is 1
The Immune Selection
• Consists of two steps:
– Perform an immunity test: If the fitness of an
individual is less than that of its parent,
degeneration occurred during crossover and
mutation. Use the parent instead of the child
– Annealing selection: an individual is selected
from the present offspring to join with the new
parents
The Algorithm
• The immune genetic algorithm
– 1. Create initial random population A1.
– 2. Abstract vaccines according to the prior knowledge.
– 3. If the current population contains the optimal
individual, then the algorithm halts.
– 4. Perform crossover on the kth parent and obtain the
results Bk.
– 5. Perform mutation on Bk to obtain Ck.
– 6. Perform vaccination on Ck to obtain Dk.
– 7. Perform immune selection on Dk and obtain the
next parent Ak+1, and then go to step 3).
Algorithm Flow
Convergence
• General GA algorithms are not guaranteed
to converge
• The IGA is convergent with a probability
of 1
The Immune Operator
• Uses the vaccination and immune selection
operators
• During these operations, the basic problem
characteristics are abstracted into a schema
• Theorem 2: Under the immune selection, if the
vaccination makes the fitness of an individual
higher than the average fitness of the current
population, then the schema of the
corresponding vaccine will be diffused at an
index level within the population. If not, it will be
restrained or attenuated by an index level
Simulations
• Simulations were performed on the
Traveling Salesman Problem (TSP)
• The following results were for the 75 city
TSP
• Were L is the side of the smallest square
containing all cities, N is the number of
cities (75), and D is the path length of the
current permutation, the fitness function
used was:
Results for GA and IGA
Fitness of GA and IGA
(Bad Vaccine)
Conclusions
• Introducing the immunity operator
guarantees convergence of the genetic
algorithm
• Proper vaccine selection causes the
algorithm to converge quickly. However,
even poor vaccine selection causes the
algorithm to converge, just more slowly
• For most large and/or complex problems,
the IGA speeds up performance drastically
Questions??