1. Dr. Unnikrishnan P.C. Professor, EEE
EE368 Soft Computing
Dr. Unnikrishnan P.C.
Professor, EEE

2. Module V
Genetic Algorithms

3. Introduction
One of the central challenges of computer science is to get a computer to do what needs to be done, without telling it how to do it.

4. History

5. Motivation & Necessity
Nature is the motivation. Nature automatically finds out the best, fittest individual from a species those who are likely to survive more easily than others. Exactly the same way we can find out the best solutions among a no of solutions by using GA from a given search space for a specific Problem.

6. Motivation & Necessity
NECESSITY: Often we need a search or an optimization process which- 1) Can deal with complex multidimensional discontinuous problem.. 2) Is easy and efficient to find global maxima or minima. 3) Faster. 4) Can be implemented by computer. 5) Can be used in Huge search space defined for those variables. GA satisfies the following criterias.

8. Introduction
• The only intelligent systems on this planet are biological. • Biological intelligences are designed by natural evolutionary processes. • They often work together in groups, swarms, or flocks. • They don't appear to use logic, mathematics, complex planning, complicated modeling of their environment. • They can achieve complex information processing and computational tasks that current artificial intelligences find very challenging indeed.

10. NATURAL INSPIRED COMPUTING
• In other words Biologically Inspired Computing. • Biological organisms cope with the demands of their environments. • They uses solutions quite unlike the traditional human engineered approaches to problem solving. • They exchange information about what they’ve discovered in the places they have visited. • Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature.

12. Classical Computation Vs Bio-Inspired Computation
• Classical computing is good at: • Number-crunching • Thought-support (glorified pen-and-paper) • Rule-based reasoning • Constant repetition of well-defined actions. • Classical computing is bad at: • Pattern recognition • Robustness to damage • Dealing with vague and incomplete information; • Adapting and improving based on experience

13. Classical Computation Vs Bio-Inspired Computation
• Bio-inspired computing takes a more evolutionary approach to learning. • In traditional AI, intelligence is often programmed from above. The Programmer create the program and imbues it with its intelligence. • Bio-inspired computing, on the other hand, takes a more bottom-up, decentralized approach. • Bio-inspired computing often involve the method of specifying a set of simple rules, a set of simple organisms which adhere to those rules.

14. Genetic Algorithm-Evolution

15. Genetic Algorithm-Evolution
• Each species try to adapt with the gradually changing environment on the earth. • The knowledge that each species gains is encoded in its chromosomes automatically, which undergoes transformations when reproduction occurs. • Over a period of time, these changes to the chromosomes give rise to more fit species that are more likely to survive, and so have a greater chance of passing their improved characteristics on to future generations. • Otherwise the species may extinct. • Genetic Algorithm is search Heuristic that mimics the process of natural evolution and helps us to find out the fittest solution of a problem exactly the same way simulating selection, crossover, mutation etc. • Often used in optimization and search problems.

16. Evolution In The Real World
• Each cell of a living thing contains chromosomes - strings of DNA. • Each chromosome contains a set of genes - blocks of DNA. • Each gene determines some aspect of the organism (like eye colour). • A collection of genes is sometimes called a genotype. • A collection of aspects (like eye characteristics) is sometimes called a phenotype.

17. Evolution In The Real World
• Reproduction involves recombination of genes from parents and then small amounts of mutation (errors) in copying. • The fitness of an organism is how much it can reproduce before it dies. • Evolution based on “survival of the fittest”.