new genetic algorithm

8/14/2019 New Genetic Algorithm

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Genetic Algorithms

Presented To : Dr. Sherin Youssof

Presented By : Ahmad Abdullatif Goudah


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Out-line

2.1. A Brief History of Evolutionary Computation

2.2. Biological Terminology

2.3. Search Spaces and Fitness Landscapes

2.4. Elements of Genetic Algorithms 2.5. A Simple Genetic Algorithm

2.6. Flowchart for a schematic genetic operation

2.7. Encoding a Problem for a Genetic Algorithm

(Binary and non-binary encoding)

2.8. Examples of different problem encoding

schemes

2.9. Selection Methods


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2.1. A Brief History of Evolutionary

Computation

Evolutionary Computation is the field of studydevoted to the design, development, andanalysis is problem solvers based on naturalselection (simulated evolution).

Idea of evolutionary computing was introduced inthe 1960s by Professor Dr. Ingo Rechenberg inhis work "Evolution strategies" .

Genetic Algorithms (GAs) were invented by JohnHolland and lead to Holland's book "Adaption inNatural and Artificial Systems" published in 1975.

In 1992 "genetic programming" (GP) methodused by John Koza to evolve programs toperform certain tasks.


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Computation (cont.)

Evolutionary Computation has been successfully

applied to a wide range of problems including:

Aircraft Design

Routing in Communications

Networks Tracking Wind shear

Game Playing (Checkers [Fogel])

Robotics

Air Traffic Control

Design

Scheduling Machine Learning

Pattern Recognition

Job Shop Scheduling

Strike Force Allocation

Theme Park Tours

(Disney Land/World) VLSI Circuit Layout

Market Forecasting

Egg Price Forecasting

Design of Filters andBarriers

Data-Mining User-Mining

Resource Allocation

Path Planning

Etc...


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Computation (cont.)

Evolutionary computation comprises the four main areas of

Genetic Algorithms, Evolution Strategies, Genetic

Programming and Simulated Annealing


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2.2. Biological Terminology

Chromosome: All living organisms

consist of cells. In

each cell there is the

same set ofchromosomes.

Chromosomes are

strings ofDNA andserves as a model for

the whole organism.


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2.2. Biological Terminology (cont.)

A chromosome consist ofgenes, blocks of DNA. Each gene encodesa particular protein. Basically can be said, that each gene encodes a

trait , for example color of eyes. Possible settings for a trait (e.g. blue,

brown) are called alleles. Each gene has its own position in the

chromosome. This position is called locus.

Complete set of genetic material (all chromosomes) is called genome.

Particular set of genes in genome is called genotype. The genotype is

with later development after birth base for the organism's phenotype,

its physical and mental characteristics, such as eye color, intelligence

etc.

During reproduction, first occurs recombination (orcrossover). The

new created offspring can then be mutated. Mutation means, that the

elements of DNA are a bit changed. The fitness of an organism is

measured by success of the organism in its life.


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2.2. Biological Terminology (cont.)

Summary .. Individual - Any possible solution

Population - Group of all individuals

Search Space - All possible solutions to the problem

Chromosome - Blueprint for an individual Trait - Possible aspect of an individual

Allele - Possible settings for a trait

Locus - The position of a gene on the chromosome

Genome - Collection of all chromosomes for an individual


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2.3. Search Spaces and Fitness

Landscapes

search space ( state space ) : The space of allfeasible solutions

Each point in the search space represent one feasible

solution.

Each feasible solution can be "marked" by its value orfitness for the problem.


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2.3. Search Spaces and Fitness

Landscapes (cont.)

fitness landscape : In order to use evolutionaryoptimization, one has to define for every possible solution s

to the problem of interest how 'good' it is. This is done by

introducing a scalar-valued function f(s) which is called the

fitness function or fitness landscape.

A high f(s) implies that s is a good solution

The best solution found can survive to end of run which is

Darwinian principle of survival of the fittest
http://en.wikipedia.org/wiki/Solution_pointhttp://en.wikipedia.org/wiki/Scalar_%28mathematics%29http://en.wikipedia.org/wiki/Function_%28mathematics%29http://en.wikipedia.org/wiki/Fitness_functionhttp://en.wikipedia.org/wiki/Fitness_functionhttp://en.wikipedia.org/wiki/Function_%28mathematics%29http://en.wikipedia.org/wiki/Scalar_%28mathematics%29http://en.wikipedia.org/wiki/Solution_point


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2.4. Elements of Genetic Algorithms

1. a population of candidate solutions (CSs) is randomly generated.

2. Each of the CSs is evaluated and assigned a fitness based on a

user specified evaluation function to determine the goodness of

a CS.

3. A number of individuals are then selected to be parents based on

their fitness.

4. The Select_Parents method must be one that balances the urge

for selecting the best performing CSs with the need for populationdiversity.


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2.5. A Simple Genetic Algorithm

BEGINGenerate initial population;

Compute fitness of each individual;

REPEAT /* New generation /*

FOR population_size / 2 DO

Select two parents from old generation;

/* biased to the fitter ones */

Recombine parents for two offspring;

Compute fitness of offspring;

Insert offspring in new generationEND FOR

UNTIL population has converged

END


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2.6. Flowchart for a schematic genetic

operation


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2.7. Encoding a Problem for a Genetic

Algorithm (Binary and non-binary encoding)

Encoding of chromosomes is one of the problems, when you are starting tosolve problem with GA. Encoding very depends on the problem.

Binary Encoding

every chromosome is a string of bits, 0 or 1.

2.8.Example of Problem: Knapsack problem

The problem: There are things with given value and size. The

knapsack has given capacity. Select things to maximize the

value of things in knapsack, but do not extend knapsack

capacity.

Encoding: Each bit says, if the corresponding thing is in

knapsack.


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(cont.)

Permutation Encoding

every chromosome is a string of numbers

2.8.Example of Problem: Travelling salesman problem (TSP)

The problem: There are cities and given distances between

them.Travelling salesman has to visit all of them, but he does not

to travel very much. Find a sequence of cities to minimize travelled

distance.

Encoding: Chromosome says order of cities, in which salesman

will visit them.


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(cont.) Value Encoding

every chromosome is a string of some values.

Values can be anything connected to problem, form numbers, real

numbers or chars to some complicated objects.

2.8.Example of Problem: Finding weights for neural network

The problem: There is some neural network with givenarchitecture. Find weights for inputs of neurons to train the

network for wanted output.

Encoding: Real values in chromosomes represent

corresponding weights for inputs.


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(cont.)

Tree Encoding

every chromosome is a tree of some

objects, such as functions or

commands in programming

language. 2.8.Example of Problem: Finding a

function from given values

The problem: Some input and

output values are given. Task is to

find a function, which will give the

best (closest to wanted) output to allinputs.

Encoding: Chromosome are

functions represented in a tree.


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2.9. Selection Methods

chromosomes are selected from the population to be

parents to crossover.

There are many methods how to select the best

chromosomes, for example :

Roulette wheel selection. Boltzman selection.

Tournament selection.

Rank selection.

Steady state selection.


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2.9. Selection Methods (cont.)

Roulette wheel selection Parents are selected according to their fitness.

The better the chromosomes are, the more chances to be

selected they have.

Imagine a roulette wheel where are placed all

chromosomes in the population, every has its place bigaccordingly to its fitness function

will have problems when the fitnesses differs very much


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Rank Selection

Rank selection first ranks the

population .

Every chromosome receives

fitness from this ranking. The worst will have fitness 1,

second worst 2etc.

The best will have fitness N

(number of chromosomes in

population) . But this method can lead to

slower convergence, because

the best chromosomes do not

differ so much from other

ones.


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Steady-State Selection

Main idea of this selection is that big part of

chromosomes should survive to next generation.

In every generation are selected a few (good - withhigh fitness) chromosomes for creating a new offspring

Then some (bad - with low fitness) chromosomes are

removed and the new offspring is placed in their place.

The rest of population survives to new generation.


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Boltzmann selection

In Boltzmann selection, a method inspired by the

technique of simulated annealing, selection pressure is

slowly increased over evolutionary time to gradually

focus the search.

Given a fitness of f, Boltzmann selection assigns a new

fitness, f0, according to a differentiable function.


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Tournament Selection Involves running several "tournaments" among a few

individuals chosen at random from the population .

The winner of each tournament (the one with the best

fitness) is selected for crossover

If the tournament size is larger, weak individuals havea smaller chance to be selected.

Tournament selection pseudo code:

(k=1) selection is equivalent to random selection. Deterministic tournament selection selects the best

individual (when p=1) in any tournament


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