Introduction to Genetic Algorithm (GA) Presented By: Rabiya Khalid Department of Computer Science

GA (1/31) Introduction History Based on Darwin’s theory of evolution Rapidly growing area of artificial intelligence Used to solve optimization based problems Techniques inspired by evolutionary biology Inheritance Mutation Selection Crossover History Evolutionary computing evolved in the 1960’s by I.Rechenberg GA’s were invented by John Holland in the mid-70’s. Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence.

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GA (3/31) Lets learn biology first Our body is made up of trillions of cells Each cell has a core structure (nucleus) that contains chromosomes Each chromosome is made up of tightly coiled strands of deoxyribonucleic acid (DNA) Genes are segments of DNA that determine specific traits, such as Eye color Hair color A gene mutation is an alteration in DNA It can be inherited or acquired during your lifetime Some changes in genes result in genetic disorders.

GA (4/31) Natural selection GA is inspired from nature Only the organisms best adapted to their environment tend to survive Transmit their genetic characteristics in increasing numbers to succeeding generations Those less adapted tend to be eliminated GA is inspired from nature A genetic algorithm maintains Population of candidate solutions for the problem Makes it evolve by iteratively applying a set of stochastic operators

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GA (6/31) Key terms Individual Population Search Space Chromosome Any possible solution Population Group of all individuals Search Space All possible solutions to the problem Chromosome Blueprint for an individual Trait Possible aspect (features) of an individual Allele Possible settings of trait (black, blond, etc.) Locus The position of a gene on the chromosome Genome Collection of all chromosomes for an individual

GA (7/31) Key terms General use Role in Smart grid Individual Any possible solution On-off status of appliances in a time slot Population Group of all individuals Collection of all chromosomes Search Space All possible solutions to the problem Any values in constraint defined limits Chromosome Blueprint for an individual Duplicate values of objective function Allele Possible settings of trait (black, blond, etc.) Characteristics of defined system to satisfy optimization limits Locus The position of a gene on the chromosome Single bit (0 or 1) Genome Collection of all chromosomes for an individual Binary on /off (1/ 0) states arrays for each appliance

GA (8/31) GA requirements Representation A genetic representation of the solution domain (set search space) A fitness function to evaluate the solution domain (objective function according to system model in case of smart grid) Representation 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 ...

GA (9/31) Algorithm Initialization Generate population Evaluate fitness While gen<max_gen do Select two parents Crossover Mutation Elitism End

GA (10/31) Fitness function The fitness function is always problem dependent Assigns fitness value to the individual It summarizes, how close a solution is to achieving the set aims

GA (11/31) Selection Darwinian survival of fittest More preference to get better guys Ways to select Roulette wheel Tournament Truncation By itself, pick best Probability of selection is assigned to individuals based on their fitness value Two parents are selected randomly from set of individuals with high selection probability Selected parents are used to produce off-springs

GA (12/31) Crossover Single point crossover Two point crossover Uniform crossover

GA (13/31) Mutation Types of mutation String subjected to mutation after crossover It prevents falling all solutions into local optimum It alters bits of chromosomes to make the string better Bit wise mutation is performed with probability of mutation Po A random number ro is generated and compared with Po If Po>ro , then mutation occurs Types of mutation Bit inversion Selected bits are inverted Order changing The number are selected and exchanged

GA (14/31) Elitism Search space Elitism involves copying a small proportion of the fittest candidates, unchanged, into the next generation EA does not waste time re-discovering previously discarded partial solutions Search space The set of solutions among which the desired solution resides Best solution among a number of possible solutions Represented by one point in the search space

GA (15/31) Advantages of GA Limitations Concepts are easy to understand Always an answer; answer gets better with time Less time required for some special applications Chances of getting optimal solution are more Limitations The population considered for the evolution should be moderate or suitable One for the problem (normally 20-30 or 50-100) Crossover rate should be 80%-95% Mutation rate should be low i.e. 0.5%-1% assumed as best The method of selection should be appropriate Writing of fitness function must be accurate

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