genetics algorithms

8/3/2019 Genetics Algorithms

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Seminar on

GENETIC ALGORITHMS


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CONTENTS

Introduction

Problem Formulation

Application of genetic algorithm

Results and Discussions

Conclusions

References


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Introduction

Bioinformatics is an application of computer technology.

Time table scheduling is allocation of various resources with

some constraints.

Resources of problem are rooms,lecturers,subjects,classes.

Two constraints are Soft constraints and Hard constraints.

Usually solved in academic organisations by harnessing the

power of computers.

Genetic algorithm makes it simpler.


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Problem Formulation

Problem definitionTimetabling problem can be defined as scheduling of resources

without conflicts

* Lecturer conflictsLecturer should not be assigned with two subjects of eithersame or different classes at the same time

* Subject conflictsTwo subjects should not be taught at the same time

* Room conflictsOne room should not be allocated to two classes at the

same time


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Time tablesClass time tables

Practical / lab time tables

Combined / complete time tables

Note: here assignment refers to


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Approches to automated time tabling using

genetic algorithm

Various versions:

Hybrid genetic algorithmParallel & distributed genetic algorithm

Genetic algorithm with divide & conquer technique

Genetic algorithm with greedy methods


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Fitness functionn m l r

Fitness value = (Cs(i)+Cl(j)+Cr(k))

p=0 i=0 j=0 k=0

Where Cs - subject conflictCl - lecturer conflict

Cr - room conflict

m - number of subjects

l - number of lecturers

r - number of rooms

n - number of classes


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Chromosome Representation

Binary Encoding

Value Encoding

Tree Encoding

Permutation Encoding


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Genetic operators

Crossover operator

Selection operator

Mutation operator


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Binary Encoding

11001011 + 11011101 = 11011111

Tree Encoding


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Application of GA to time table problem

Genetic algorithm:1. Initial population.

2. Apply GA process until optimum solution is obtained

Find fitness of all chromosomes.

Select parent fit for further processing. Apply crossover on selected parents.

Apply mutation on offspring generated in crossover.

Find fitness of mutated population.

Check for optimal result if not goto step2>.

3.End.


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Chromosome

representation scheme

Value encoding

Size of genetic material 5/6

Population size Variable(30 to 150)

Number of generations Variable(upto 2000)

Selection method Random with individual

selection method

Type of crossover Variable(set by user)

Crossover probability Variable(maximize for

better result)

Crossover point Variable(random

generation and program)

Type of mutation Alternate(inversion and

order changing)

Mutation probability Variable(min. for better

results)

Mutation point Variable(random

generation in program)

Coding scheme


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Figure4:Scheduler result for population size=150

Figure5:Comparisions of crossover types

Generation/Best Fitness values


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MERITS

1. Easy to apply for scheduling the timetables.

2. Overcomes the disadvantages of manual timetables.

3. Gives the appropriate results.


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DEMERITS

1. Should have the knowledge of the biological terms.

2. Good analytical skill is required.


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REFERENCES

Mahdi, Ainon, Roziati using a genetic

algorithm optimizer tool to generate good

quality timetables.

S.Sundaragopal, Rajani Boddu heuristic

optimization:complex university level

academic scheduling using GA. Goldberg D.E GA in search,optimization

& machine learning Addison Wesley.


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Conclusion

In this study we have implemented a genetictimetable scheduler by keeping the flexibility

to user.

A good schedule can be obtained by using

Pentium 4 with 256 MB of RAM.

The scheduler can be generalized for any large

problem.

genetics algorithms

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