Lecture2 genetic algorithmAlgorithm

7/29/2019 Lecture2 genetic algorithmAlgorithm

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Genetic Algorithms are good

at taking large, potentially

huge search spaces and

navigating them, lookingfor optimal combinations

of things, solutions you

might not otherwise find in

a lifetime.

- Salvatore Mangano

Computer Design, May 1995

Genetic Algorithms:


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The Genetic Algorithm

Provide efficient, effective techniques for

optimization and machine learning

applications

Widely-used today in business, scientific andengineering circles


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Introduction to Evolutionary Computation

Evolutionary Computation is the field of study devoted to the

design, development, and analysis is problem solvers based onnatural selection (simulated evolution).

Evolution has proven to be a powerful search process.

Evolutionary Computation has been successfully applied to a

wide range of problems including:

Aircraft Design,

Routing in Communications Networks,

Tracking Windshear,

Game Playing (Checkers)


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Main idea

Take a population of candidate solutions to a

given problem

Use operators inspired by the mechanismsof natural genetic variation

Apply selective pressure toward certain

properties

Evolve a more fit solution


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Evolutionary terminology

Abstractions imported from biology

Chromosomes, Genes, Alleles

Fitness, Selection

Crossover, Mutation


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GA terminology

In the spirit but not the letter of biology

GA chromosomes are strings of genes

Each gene has a number of alleles; i.e., settings

Each chromosome is an encoding of a solution

to a problem

A population of such chromosomes is operated

on by a GA


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Encoding

A data structure for representing candidate

solutions

Often takes the form of a bit string

Usually has internal structure; i.e., different parts

of the string represent different aspects of the

solution)


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Fitness

A measure of the goodness of the organism

Expressed as the probability that the organism will live

another cycle (generation)

Basis for the natural selection simulation

Organisms are selected to mate with probabilities

proportional to their fitness

Probabilistically better solutions have a better chance of

conferring their building blocks to the next generation (cycle)


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Simple Genetic Algorithm{

initialize population;

evaluate population;

while TerminationCriteriaNotSatisfied{

select parents for reproduction;

perform recombination and mutation;

evaluate population;}

}


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Candidate Solutions CS1. In an Evolutionary Computation, 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.

1. The evaluation function is used 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

population diversity.


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Parent selection

Chance to be selected as parent proportional to

fitness

Roulette wheel

To avoid problems with fitness function

Tournament


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Tournament Binary tournament

Two individuals are randomly chosen; the fitter of the twois selected as a parent

Probabilistic binary tournament

Two individuals are randomly chosen; with a chancep,

0.5


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The GA Cycle of Reproduction

reproduction

population evaluation

modification

discard

deleted

members

parents

children

modified

children

evaluated children


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Parents and Generations

1. The selected parents are allowed to create a set of offspring

which are evaluated and assigned a fitness using the same

evaluation function defined by the user.

2. Finally, a decision must be made as to which individuals of

the current population and the offspring population should be

allowed to survive.

1. Typically, in EC , this is done to guarantee that the

population size remains constant.


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Population

Chromosomes could be:

Bit strings (0101 ... 1100) Real numbers (43.2 -33.1 ... 0.0 89.2)

Permutations of element (E11 E3 E7 ... E1 E15)

Lists of rules (R1 R2 R3 ... R22 R23)

Program elements (genetic programming) ... any data structure ...

population


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Reproduction

reproduction

population

parents

children

Parents are selected at random with

selection chances biased in relation tochromosome evaluations.


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Crossover: Recombination

P1 (0 1 1 0 1 0 0 0) (0 1 0 0 1 0 0 0) C1

P2 (1 1 0 1 1 0 1 0) (1 1 1 1 1 0 1 0) C2

Crossover is a critical feature of genetic

algorithms:

It greatly accelerates search early in evolution of a

population

It leads to effective combination of schemata(subsolutions on different chromosomes)

*


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Mutation: Local Modification

Before: (1 0 1 1 0 1 1 0)

After: (0 1 1 0 0 1 1 0)

Before: (1.38 -69.4 326.44 0.1)

After: (1.38 -67.5 326.44 0.1)

Causes movement in the search space(local or global)

Restores lost information to the population


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Evaluation

The evaluator decodes a chromosome and assigns

it a fitness measure

The evaluator is the only link between a classical

GA and the problem it is solving

evaluation

evaluated

children

modified

children


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Fitness Function

Purpose Parent selection

Measure for convergence

Steady state: Selection of individuals to die

Should reflect the value of the chromosome in some real way

Next to coding the most critical part of a GA


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Selecting and Stopping Once a decision is made the survivors comprise the next

generation (Pop(t+1)).

This process of selecting parents based on their fitness,allowing them to create offspring, and replacing weaker

members of the population is repeated for a user specifiednumber of cycles.

Stopping conditions for evolutionary search could be:

The discovery of an optimal or near optimal solution

Convergence on a single solution or set of similar solutions,

When the EC detects the problem has no feasible solution,

After a user-specified threshold has been reached, or

After a maximum number of cycles.


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Issues for GA Practitioners

Choosing basic implementation issues:

representation

population size, mutation rate, ...

selection, deletion policies

crossover, mutation operators

Termination Criteria

Performance, scalability

Solution is only as good as the evaluation function(often hardest part)


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Benefits of Genetic Algorithms

Concept is easy to understand

Modular, separate from application

Supports multi-objective optimization

Good for noisy environments Always an answer; answer gets better with time

Inherently parallel; easily distributed

Many ways to speed up and improve a GA-based

application as knowledge about problem domain isgained

Easy to exploit previous or alternate solutions


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When to Use a GA

Alternate solutions are too slow or overly complicated

Need an exploratory tool to examine new approaches

Problem is similar to one that has already been

successfully solved by using a GA

Benefits of the GA technology meet key problem

requirements


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Some GA Application Types

Domain Application TypesControl gas pipeline, pole balancing, missile evasion, pursuit

Design semiconductor layout, aircraft design, keyboardconfiguration, communication networks

Scheduling manufacturing, facility scheduling, resource allocation

Robotics trajectory planning

Machine Learning designing neural networks, improving classificationalgorithms, classifier systems

Signal Processing filter design

Game Playing poker, checkers, prisoners dilemma

Combinatorial

Optimization

set covering, travelling salesman, routing, bin packing,graph colouring and partitioning

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Lecture2 genetic algorithmAlgorithm