GENETIC ALGORITHM
- 1. GENETIC ALGORITHM Submitted by, M. Dharani., B.Sc., Nadar Saraswathi college of Arts & Science, Theni.
- 2. INTRODUCTION Genetic algorithm is a search based optimization technique based on the principles of genetics and natural selection. It is frequently used to find optimal or near optimal solutions to difficult problems which otherwise would take a lifetime to solve.
- 3. INTRODUCTION TO OPTIMIZATION Optimization is the process of making something better. process Set of inputs Set of outputs
- 4. BASIC TERMINOLOGY POPULATION: It is a subset of all the possible(encoded) solutions to the given problem. CHROMOSOMES: A chromosome is one such solution to the given problem. Gene: A gene is one element position of a chromosome. ALLELE: It is the value a gene takes for a particular chromosome.
- 5. PHENOTYPE: Phenotype is the population in the actual real world solution space in which solutions are represented in a way they are represented in real world situations. ENCODING AND DECODING: For simple the phenotype and genotype space are the same.
- 6. GENETIC OPERATION: These alter the genetic compositions of the offspring's. These include crossover, mutation, selection, etc. FITNESS FUNCTION: A fitness function is a function which takes the solution as inputs and produces the suitability of the solution as the output.
- 7. BASIC STRUCTURE OF GENETIC ALGORITHM Population initialization Fitness function calculation Cross over Mutation Survivor selection Terminates and return best Loop until termination critic reached
- 8. GENOTYPE REPRESENTATION BINARY REPESENTATION REAL VALUED REPRESENTATION 0 0 1 0 1 1 1 0 0 1 0.5 0.2 0.6 0.8 0.7 0.4 0.3 0.2 0.1 0.9
- 9. GA POPULATION Population is a subset of solutions in the current generation. Set of Chromosomes. POPULATION INITIALIZATION : A. Random Initialization B. Heuristic Initialization POPUATION MODEL: A. Steady state B. Generational
- 10. FITNESS FUNCTION Takes a candidate solution to the problem as input and produces as output. The objectives is to either maximize or minimize the given objective function.
- 11. GA—PARENT SELECTION CHROMOSOME FITNESS VALUE A 8.2 B 3.2 C 1.4 D 1.2 E 4.2 F 0.3
- 12. GA--MUTATION Used to maintain and introduce diversity in the genetic population. Mutation Operations: Bit Flip Mutation 0 0 1 1 0 1 0 0 1 0
- 13. GA—TERMINATION CONDITION When there has been no improvement in the population for X iterations. When we reach an absolute number of generations. When the objective function value has reached a certain pre-defined value.
- 14. THANK YOU!!!