Biologically inspired algorithms
BY:
Andy Garrett
YE Ziyu
What is Evolutionary Computation
• A subfield of artificial intelligence which mimics biology
• Used in optimization of black box problems
• Parallel processing
Types of Evolutionary Computation
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Evolutionary programing
Genetic algorithms
Evolutionary strategies
Genetic programing
• Genetic algorithms
• Swarm intelligence
Genetic Algorithms
Genetic Algorithm——what is gene?
Biology:
A certain DNA sequence
at a certain position
of the chromosome.
Genetic Algorithm :
A certain value
of a certain element
of the solution.
A certain element (an allele)
of the solution (the chromosome)
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Three alternative values (genes)
Genetic Algorithm——what is gene?
Biology
Genetic Algorithm
Genes
Genes
constitute
Chromosome
determines
Fitness of a individual
In the environment
constitute
Solution
determines
Performance of a solution
in the problem. (Fitness)
Genetic Algorithm——what is gene?
In Genetic Algorithm, genes (values of elements of the solution)
determine the fitness (performance) of a solution.
To solve a problem
=
To find the combination of genes
that provides the best fitness (performance)
Genetic Algorithm——Initiation
X
To conduct evolution,
We need a set of solutions.
(A population)
Initially, the population is
generated randomly. This is
the first generation.
Y
A two-dimension search space
dotted by randomly generated solutions
(each solution consists of two elements,
x and y)
Genetic Algorithm——Reproduction: Crossover
Crossover is how we create new individuals from
the existing ones.
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Two solutions
Randomly select
somehow be
one (or more)
selected as “parents”
point
Apply cross
(Recombine the
two solutions)
Finish！
These will be two
Individuals in
the next generation
Genetic Algorithm——Reproduction: Selection
• Individuals with higher fitness
have a higher probability to be
chosen as parents of the
crossover operation.
• Survival of the fittest
Genetic Algorithm——Reproduction: Selection
What’s the effect?
Genes associated with high fitness are more likely to be
passed to the new generation.
After some generations, the average fitness of the
population gets improved!
Genetic Algorithm——Reproduction: Selection
In a graphic view: (use our two-dimension example)
X
The population gathers around
the optimal solution.
It’s like that the population is
climbing the hill.
Problem solved?
Y
Genetic Algorithm——Mutation
Problem: What if we have multiple hills in the searching
X
space?
(Global optimum)
The individuals may climb onto
a hill that is not the highest.
Thus, they may gather around
a local optimum.
(Local optimum)
Y
Genetic Algorithm——Mutation
According to the crossover operation, genes in the new
X
generation only come from the
previous generation.
Thus, once the solutions gather
around a local optimum, they
will be constrained in its vicinity!
They won’t find the global optimum.
(Constraining region)
Y
Genetic Algorithm——Mutation
Mutation: Make random changes to some genes in
X
each generation.
NEW genes are created!
Solutions can jump out of the
region.
After some generations, they may
probably gather around the
global optimum.
Y
Genetic Algorithm——Scenario
Step 1: Initiation (Randomly generate the first generation);
Step 2: Mutation;
Step 3: Fitness evaluation;
Step 4: Reproduction:
Selection;
Crossover;
Step 5: Go back to step 2, repeat this loop until a
sufficiently good solution is found.
Swarm intelligence
Swarm Intelligence
Swarm intelligence
=
cognition of individuals + communication
Application in optimization problems:
Particle Swarm Optimization (PSO)
Swarm Intelligence——Initiation
Randomly generate
a set of solutions
(called a swarm of particles),
their initial positions,
and their initial speeds.
X
V3o
V2o
V1o
Y
Swarm Intelligence——Travelling
Two forces are exerted
on each particle:
1. Force pointing to the best
solution this particle has ever
passed through (pbest)
2. Force pointing to the best
solution any particle has ever
passed through (gbest)
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pbest2(gbest)
pbest3
pbest1
pbest
gbest
Y
Swarm Intelligence——Travelling
Forces pointing to pbests:
Fp1, Fp2, Fp3
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Fp2
These forces result from the
cognition of individual particles.
Fp3
Fp1
Y
Swarm Intelligence——Travelling
Forces pointing to gbests:
Fg1, Fg2, Fg3
These forces result from the
communication among the particles.
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Fg3
Fg2
Fg1
Y
Swarm Intelligence——Travelling
After some time, the
particles would probably
find some solutions that
are sufficiently close the
global optimum.
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Fp2
Fg3
Fp3
Fg2
Fg1
Fp1
Y
https://www.youtube.com/watch?v=j028fsZZZI4
Evolutionary Computation
• Time complexity is not generally considered
• Number of iterations required for convergence
Questions?