7.GenAlgo Intro

8/3/2019 7.GenAlgo Intro

1/16

Genetic Algorithms

[email protected]


2/16

Genetic Algorithm Is...

Computer algorithm

That resides(based) on principles of genetics and evolution


3/16

Genetic Algorithm (3) Basic algorithm

Outline of the basic algorithm

0 START : Create random population of n chromosomes

1 FITNESS : Evaluate fitness f(x) of each chromosome in

the population

2 NEW POPULATION

0 SELECTION : Based on f(x)

1 RECOMBINATION: Cross-over chromosomes

2 MUTATION : Mutatechromosomes

3 ACCEPTATION : Reject or accept new one

3 REPLACE : Replace old with new population: the new

generation

4 TEST : Test problem criterium

5 LOOP : Continue step 1 4 until criterium is

satisfied


4/16

Initialization

Start with a population ofrandomly generated individuals, or

use - A previously saved population- A set of solutions provided by

a human expert

- A set of solutions provided byanother heuristic algorithm


5/16

Selection

The selection operation copies a single individual,probabilistically selected based on fitness,into the next generation of the population.

There are few possible ways to implement selection:

Only the strongest survive

Choose the individuals with the highest fitnessfor next generation

Some weak solutions survive

Assign a probability that a particular individual

will be selected for the next generation More diversity

Some bad solutions might have good parts!


6/16

Fitness Selection

Each successive generation the selectedpopulation goes for new population generation.

Selection is based on the fitness function Roulette wheel selection

Rank based selection

Tournament selection

Steady-state selection

Truncation selection


7/16

Crossover

Crossover is concept from genetics.

Crossover is reproduction.

Crossover combines genetic material from two parents,

in order to produce superior offspring. Few types of crossover:

One-point

Multiple point

Uniform


8/16

One-point Crossover

Parent #1 Parent #2

0

1

5

3

5

4

7

6

7

6

2

4

2

3

0

1


9/16

One-point Crossover

Parent #1 Parent #2

0

1

5

3

5

4

7

6

7

6

2

4

2

3

0

1


10/16

One-point crossover 1

Randomly one position in the chromosomes is chosen

Child 1 is head of chromosome of parent 1 with tail of chromosome ofparent 2

Child 2 is head of 2 with tail of 1

Parents: 1010001110 0011010010

Offspring: 0101010010 0011001110

Randomly chosen position


11/16

Two-point crossover

Parents: 1010001110 0011010010

Offspring: 0101010010 0011001110

Randomly chosen positions

Randomly two positions in the chromosomes are chosen

Avoids that genes at the head and genes at the tail of achromosome are always split when recombined


12/16

Uniform crossover

A random mask is generated

The mask determines which bits are copied from one parent andwhich from the other parent

Bit density in mask determines how much material is taken fromthe other parent (takeover parameter)

Mask: 0110011000 (Randomly generated)

Parents: 1010001110 0011010010

Offspring: 0011001010 1010010110


13/16

Mutation

Mutation introduces randomness into the population.

The idea of mutationis to reintroduce divergenceinto a converging population.

Mutation is performedon small part of population,

in order to avoid entering unstable state.


14/16

Mutation

Purpose: to simulate the effect of errors thathappen with low probability during

duplication

Results:

- Movement in the search space- Restoration of lost information to thepopulation


15/16

Mutation...

1 1 0 1 0 10 0

0 1 0 1 0 10 1

1 0

0 1

Parent

Child


16/16

Advantages of GAs

Concept is easy to understand.

Minimum human involvement.

Computer is not learned how to use existing solution,but to find new solution!

Modular, separate from application

Supports multi-objective optimization

Always an answer; answer gets better with time !!!

Inherently parallel; easily distributed

Many ways to speed up and improve a GA-based application asknowledge about problem domain is gained

Easy to exploit previous or alternate solutions

Documents

7.GenAlgo Intro