It will help you in understanding about genetic algorithm and more

1. GENETIC
ALGORITHM
Presented By:-
Puneet Kulyana
M.Sc. Bioinfo.3rd Sem.
JMI,NewDelhi

2. GA Quick Overview
Developed: USA in the
1970’s
By J. Holland,
K. DeJong, D. Goldberg
• Discrete optimizationTypically applied to:
• Not too fast
• Good heuristic for combinatorial problems
Attributed features:
• Traditionally emphasizes combining
information from good parents
(crossover)
• many variants, e.g., reproduction
models,
operators
Special Features:

3. What is GA??
A genetic algorithm (or GA) is a search
technique used in computing to find true or
approximate solutions to optimization and
search problems.
Genetic algorithms are categorized as
global search heuristics.
Genetic algorithms are a particular class of
evolutionary algorithms that use techniques
inspired by evolutionary biology such as
inheritance, mutation, selection, and
crossover (also called recombination).

4. BASIC CONCEPTS
GA converts design space into genetic space
• Works with a coding variables
• Traditional optimization techniques are deterministic in
nature, but GA uses randomized operators
• Three important aspects:
1. Definition of objective function
2. Definition and implementation of genetic representation
3. Definition and implementation of genetic operators

5. ENCODING
• Encoding of chromosomes is one of the problems
when one is starting to solve problem with genetic
algorithm
• There are various types of encoding used for solving
the problems
• Four types of encoding are:-
1) Binary encoding
2) Permutation encoding
3) Value encoding
4) Tree encoding

6. REPRESENTATION (ENCODING) EXAMPLES
Possible individual’s encoding
• Binary (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)
• Octal
• Hexadecimal
• Floating-point
• Tree Representation
• Random Keys
• ... any data structure ...

7. SEARCH SPACE
• The space for all possible feasible solutions
• Each feasible solution can be "marked" by its value of the
fitness of the problem
• GA is started with a set of solutions called populations
• Used to form a new population
• More suitable, more chances to select
• This is repeated until best population is obtained

8. FITNESS FUNCTION
 Basically, a fitness function is used to evaluate
phenotypes to identify the fittest population members.
 It is a function which takes the solution as input and
produces the suitability of the solution as output
 In some cases, the fitness function and the objective
function may be the same, while in others it may be
different based on the problem.
 It measures the quality of the represented solution
 It is always problem dependent
 The fitness of a solution is measured, how best it gives
the result
 For instance, Knapsack problem

9. STEPS OF GENETIC ALGORITHM
• Step 1: Encoding of the problem in a binary string
• Step 2: Random generation of a population
• Step 3: Calculate fitness of each solution
• Step 4: Select pairs of parent strings based on fitness
• Step 5: Generate new string with crossover and mutation
until a new population has been produced
Repeat steps 2 to 5 until satisfying solution is obtained

10. OPERATORS OF GENETIC ALGORITHM
 Three Basic operators are:
1. Reproduction
2. Crossover
3. Mutation
 The new population is further evaluated and
tested for termination
 If the termination criteria are not met, the
population is iteratively operated
 One cycle of operations and the subsequent
evaluation– Generation in GA

11. REPRODUCTION
 Chromosomes are selected from the population to be
parents to crossover and produce offspring
 Also known as Selection Operator
 Parents are selected according to their fitness
 There are many methods to select the best chromosomes
A) Roulette Wheel Selection
B) Rank Selection
C) Tournament Selection
D) Steady State Selection
• The better the chromosomes are, the more chances to be
selected they have

12. CROSSOVER
Chromosomal
crossover (or crossing over)
is the exchange of genetic
material between
homologous chromosomes
Crossover usually occurs
when matching regions on
matching chromosomes
break and then reconnect
to the other chromosome.

13. GA OPERATORS: CROSSOVER
• Choose a random point on the two parents
• Split parents at this crossover point
• Create children by exchanging tails
• Generally setting crossover probability with a value 0.5
produces good results and its typical range id 0.3 to 1.
• E.g. Probability of crossover is 25% so 5 out of 20
chromosome will undergo crossover.

14. DIFFERENT KIND OF CROSSOVERS
• Sub-tour exchange
• Heuristic crossover
• Arithmetic Crossover
• Directional Crossover
• Single-point
• Double-point
• Multiple-point
• Uniform
• Matrix Crossover
• Random
• Permutation-based
• Order-based
• Position-based
• Cycle Crossover

15. CROSSOVER

16. MUTATION IN GENETIC
ALGORITHM
Mutation is a genetic operator used to
maintain genetic diversity from one
generation of a population of genetic
algorithm chromosomes to the next.
It is analogous to biological mutation.
The mutation probability is kept quite low
between 0.001 and 0.01.
Mutation alters one or more gene values
in a chromosome from initial state

17. DIFFERENT KIND OF MUTATION
• Uniform Mutation
• Boundary
• Non-uniform
• 1’s Complement Operator
• Logical Bitwise
• Bitwise XOR
• Bitwise OR
• Shift Operator (>>,<<)
• Masking Operator
• Inversion
• Insertion
• Displacement
• Reciprocal Exchange

18. APPLICATIONS
 There are various applications of genetic algorithm in
bioinformatics. Some of them are:-
1. They are used for multiple sequence alignment
2. Used for RNA structure prediction
3. Used for motif discovery
4. Used for building phylogenetic trees
5. Used for clustering to optimize a wide range of fit – funcions
6. Used for gene expression profiling analysis
7. Used for protein folding and protein/ligand docking
8. Used for operon prediction

19. ADVANTAGES
 Easy to understand
 Modular, separate from application
 Supports multi-objective optimization
 Easy to exploit previous or alternate solutions
 Flexible in forming building blocks for hybrid applications
 Substantial history and range of use
DISADVANTAGES
 GA requires more computational time
 It is slower than some other methods

20. REFERENCES
 Genetic algorithms in search, optimization, and
machine learning (Book by David E. Goldberg)
 ocw.mit.edu(MIT OPEN COURSE)
 nptel.ac.in
 www.google.com
 Neural Networks, Fuzzy Logic, Algorithms
- S. Rajasekaran
- G. A. Vijayalakshmi Pai

21. THANK YOU