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

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

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 1342 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

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  001 011 100 Value encoding assign value to each chromosome (value could be integer, real, character) Chromosome A: 1.234 3.443 4.322 2.335 Tree encoding Every chromosome is tree of some objects

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 ∑

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 0110113, lot of computation time lot of computation time 1100024, 010008, 10011 19 b. Evaluate the fitness according to F(x) =x2 13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 ∑

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