Evolutionary computation
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Evolutionary Computation. 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 Computation

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Evolutionary computation

Evolutionary Computation

Biologically inspired algorithms

BY:

Andy Garrett

YE Ziyu


What is evolutionary computation

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

Types of Evolutionary Computation

  • Evolutionary programing

  • Genetic algorithms

  • Evolutionary strategies

  • Genetic programing

  • Genetic algorithms

  • Swarm intelligence


Genetic algorithms

GeneticAlgorithms


Evolutionary computation

Genetic Algorithm——what is gene?

Biology:

A certain DNA sequence

at a certain position

of the chromosome.

A certain element (an allele)

of the solution (the chromosome)

Genetic Algorithm :

A certain value

of a certain element

of the solution.

Three alternative values (genes)


Evolutionary computation

Genetic Algorithm——what is gene?

Biology

Genetic Algorithm

Genes

Genes

constitute

constitute

Chromosome

Solution

determines

determines

Performance of a solution

in the problem. (Fitness)

Fitness of a individual

In the environment


Evolutionary computation

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)


Evolutionary computation

X

Genetic Algorithm——Initiation

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)


Evolutionary computation

Genetic Algorithm——Reproduction: Crossover

Crossover is how we create new individuals from

the existing ones.

Two solutions

somehow be

selected as “parents”

Randomly select

one (or more)

point

Apply cross

(Recombine the

two solutions)

Finish!

These will be two

Individuals in

the next generation


Evolutionary computation

Genetic Algorithm——Reproduction: Selection

  • Individuals with higher fitness have a higher probabilityto be chosen as parents of thecrossover operation.

  • Survival of the fittest


Evolutionary computation

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!


Evolutionary computation

Genetic Algorithm——Reproduction: Selection

In a graphic view: (use our two-dimension example)

The population gathers around

the optimal solution.

It’s like that the population is

climbing the hill.

Problem solved?

X

Y


Evolutionary computation

Genetic Algorithm——Mutation

Problem: What if we have multiple hills in the searching

space?

The individuals may climb onto

a hill that is not the highest.

Thus, they may gather around

a local optimum.

X

(Global optimum)

(Local optimum)

Y

Y


Evolutionary computation

Genetic Algorithm——Mutation

According to the crossover operation, genes in the new

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.

X

(Constraining region)

Y


Evolutionary computation

Genetic Algorithm——Mutation

Mutation: Make random changes to some genes in each generation.

NEW genes are created!

Solutions can jump out of the

region.

After some generations, they may

probably gather around the

global optimum.

X

Y


Evolutionary computation

Genetic Algorithm——Scenario

Step 1: Initiation(Randomly generate the first generation);

Step 2: Mutation;

Step 3: Fitnessevaluation;

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


Evolutionary computation

Swarm Intelligence

Swarm intelligence

=

cognition of individuals + communication

Application in optimization problems:

Particle Swarm Optimization (PSO)


Evolutionary computation

Swarm Intelligence——Initiation

Randomly generate

a set of solutions

(called a swarm of particles),

their initial positions,

and their initial speeds.

X

V2o

V3o

V1o

Y


Evolutionary computation

Swarm Intelligence——Travelling

Two forces are exerted

on each particle:

X

pbest2(gbest)

1. Force pointing to the best

solution this particle has ever

passed through (pbest)

pbest3

2. Force pointing to the best

solution any particle has ever

passed through(gbest)

pbest1

Y

pbest

gbest


Evolutionary computation

Swarm Intelligence——Travelling

Forces pointing to pbests:

Fp1, Fp2, Fp3

These forces result from the

cognition of individual particles.

X

Fp2

Fp3

Fp1

Y


Evolutionary computation

Swarm Intelligence——Travelling

Forces pointing to gbests:

Fg1, Fg2, Fg3

These forces result from the

communication among the particles.

X

Fg3

Fg2

Fg1

Y


Evolutionary computation

Swarm Intelligence——Travelling

After some time, the

particles would probably

find some solutions that

are sufficiently close the

global optimum.

X

Fp2

Fg3

Fg2

Fg1

Fp3

Fp1

Y

https://www.youtube.com/watch?v=j028fsZZZI4


Evolutionary computation1

Evolutionary Computation

  • Time complexity is not generally considered

  • Number of iterations required for convergence


Questions

Questions?


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