2. Genetic Algorithm
Soft Computing: use of inexact t solution to compute hard task problems.
Soft computing tolerant of imprecision, uncertainty, partial truth , approximation
Component of Soft Computing
 Neural network
 Support vector machine
 Fuzzy logic
 Evolutionary computation (Genetic algorithm, Differential evolution)
 Meta heuristic and Swarm Intelligence
o Ant colony optimization
o Practice swarm optimization
o Firefly algorithm
o Cuckoo search
 Ideas about probability
o Bayesian network
 Chaos theory
Hard Computing
Soft computing
Require precisely state analytical model,
Lot of computation time
Tolerant of imprecision uncertainty
partial truth approximation, human
mind
Binary logic crisp system, numerical
analysis
Based on fuzzy logic neural net
probabilistic reasoning
Precision and category
Approximate and dis positionality
Un tractability, high cost
Tractability, lower cost, high machine
intelligent, economy of communication
Require program
Evolve its own programs
Two value logic
Multi value or fuzzy
Deterministic
Stochastic
Exact input
Ambiguity and noise
sequential
Distributed parallel
Produce precise answer
Yield approximate answer

3. Evolutionary computing
Evolutionlong time scale process that change a population of organism by
generating better offspring reproduction
Search optimization algorithm
 Genetic algorithm
 Genetic programming
Fundamental of GA
GAadaptive heuristic search algorithm based on the evolutionary idea of natural
selection and genetics
Why GA
 Way of solving problem
 Adaptive heuristic search
 Intelligent exploitation of random search
Optimization
Process of finding best optimal solution for problem
Biological
 Organism set of rules living organism is cell
 Cell contain chromosomes
 Chromosome is set block of DNA
 Gene encode partial trait (feature) e.g. eye color
 Possible set of gene is alleles
 Gene has it position in chromosome called locus
 Complete set of genetic matter (all chromosome ) is called genome
 Particular set of genes in genome is called genotype
 When two chromosome mate they share their genes recombination (cross over)
new created offspring can then be mutated
 Fitness of an organism is measure by success of organism (survival)
GA disadvantage
1. No grantee of optimal solution in finite time
2. Weak theoretical basis
3. Interdependency of genes
4. Parameter tuning is an issue
5. Often computationally expensive slow

4. GA advantage
 Robust search technique
 No little knowledge the problem space
 Fairly simple to develop low develop cost
 Easy to incorporate with other method
 Solution are interpretable
 Can be run interactively
 Provide many alternative solutions
 Acceptable performance at acceptable cost on wide range of problem
 Intrinsic parallelism
Working principal
Chromosomeset of genes , solution eg ABDF
Genepart of solution e.g. 1 or 3
Individualsame as chromosome
Fitnessvalue set to individual based on how far or close individual from solution
Greater fitness value better solution is
Fitness function function assign fitness value to individual
Mutationchange random gene in an individual
Selectionselect individual for creating next generation
Outline of basic Genetic Algorithm
1- Start generate random population of n chromosome
2- Fitness evaluate the fitness of each chromosome in population
3- New population
a. Selection select two parent chromosome for population according to their
fitness
b. Crossover the parent to generate the new child (offspring)
c. Mutation
d. Acceptance pale new offspring to new population
4- Test if end condition satisfy stop and return to best solution
5- Else Loop goto step 2
Flow chart

5. Encoding
Encode solution to form that is easily process by computer
 Encode to binary form
 Encode to integer form
 Encode to real number
Example
134 
Value encoding assign value to each chromosome (value could be integer, real,
character)
Chromosome A:
Tree encoding
Every chromosome is tree of some objects

6. Reproduction or Selection
 First operator apply to population is selection depend on survivor of the fitness
(Darwin’s evolution) survival of the fitness
 Fitness function qualify the optimality of solution
Most common used method for selecting chromosome for parent to crossover are:
1. Roulette wheel
2. Tournament selection
3. Rank selection
4. Steady state section
5. Boltzmann selection
Roulette wheel
Also called fitness proportionally selection
 Chance of individual to be select is depend proportionally to its fitness greater or
less than its competitors fitness
 Like game of roulette wheel simulate
Example
 Roulette simulate 8 individual with fitness Fi
 Ex 5th individual has higher fitness function
 Fitness of individual is calculated as the wheel is spun n=8 times
 Probability of ith string is ∑

7. Evolutionary algorithm is to maximize the function F(x) =x1 with x in the integer
[0,31] i.e., x= 0 ,1,2,3,4,5,….31
Sol
1. Encode the chromosome use binary representation for integer 5 bits
2. Assume population size is 4
3. Generate initial population at random they are chromosome or genotype eg.
01101, 11000, 01000,10011
4. Calculate the fitness value for each individual
a. Decode individual to integer called phenotypes 13, lot of
computation time lot of computation time 1100024, 010008, 10011 19
b. Evaluate the fitness according to F(x) =x 169, 24576, 864,
19361
5. Select parent (two individual ) for crossover based on their fitness in pi; out of
many methods for selecting the best chromosome, if roulette-wheel selection is
used then the probability of the ith string in the population is ∑

8. Tournament Selection Example
Tournament probability = (2n-2m+1)/n2 n population size
Rank Selection
Previously problemfitness differ very much example if the best chromosome is 90%
fitness of the roulette wheel then the other chromosomes have very few chances to be
selected
RankThe worst will have fitness 1 second worst 2 etc the best has N (number of
chromosome in population )
 Slower convergence because best chromosome do not differ so much from other