BIO-Inspired Algorithms And Its Application Thomas K P Asst.Professor EEE RSET

1. BIO-Inspired Algorithms And Its Application Thomas K P Asst.Professor EEE RSET

2. Evolution Swarm Based Ecology GA GP ES DE PFA Biogeography BBO Natural River System Convergent Social Phenomenon in animals and microbes Human Immune System Weed colony AWC IWD AIS Symbiosis PS2O producer-scrounger GSO Bird Flocking PSO Stignergy ACO Fish Schooling FSA Bacterial Foraging BFA Fire Fly FA Social behavior of Bees ABC Frog Leaping algorithm SFLA BIO- Inspired Algorithms

3. Genetic Algorithms

4. Soft Computing Techniques

7. Advantages of Evolutionary Computation • Conceptual simplicity • Broad applicability • Hybridization with other methods • Parallelism • Robust to dynamic changes

8. Flow chart of an evolutionary algorithm

9. Genetic Algorithms - History • Pioneered by John Holland in the 1970’s. • Got popular in the late 1980’s. • Based on ideas from Darwinian Evolution. • Can be used to solve a variety of problems that are not easy to solve using other techniques.

10. Genetic Algorithms • An algorithm is a set of instructions that is repeated to solve a problem. • A genetic algorithm conceptually follows steps inspired by the biological processes of evolution. • Genetic Algorithms follow the idea of SURVIVAL OF THE FITTEST- Better and better solutions evolve from previous generations until a near optimal solution is obtained.

11. Genetic Algorithm • Also known as evolutionary algorithms, genetic algorithms demonstrate self organization and adaptation similar to the way that the fittest biological organism survive and reproduce. • A genetic algorithm is an iterative procedure that represents its candidate solutions as strings of genes called chromosomes. • Generally applied to spaces which are too large

12. Genetic Algorithm • A genetic algorithm is a search technique used in computing to find true or approximate solution to optimization and search problem. • GAs are categorized as global search heuristics. • GAs are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance selection crossover (recombination) and mutation.

13. Genetic Algorithm • The new population is used in the next iteration of the algorithm. • The algorithm terminates when either a maximum number of generations has been produced ,or a satisfactory fitness level has been reached for the population. • The evolution usually starts from a population of randomly generated individuals and happens in generations.

14. Genetic Algorithm • In each generation ,the fitness of every individual in the population is evaluated , multiple individuals are selected from the current population (based on their fitness) , and modified to form a new population.

15. Evolution in the real world • Each cell of a living thing contains chromosomes - strings of DNA (Dioxy Ribo Nucleic Acid) • 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 colour) is sometimes called a phenotype • 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”

16. Concepts • Individual: An individual is a single solution. • Population: Group of all individuals • Chromosome: Genes joined together to form a string of values called chromosome. • Gene: A solution to problem represented as a set of parameters ,these parameters known as genes. • Fitness score (value): Every chromosome has fitness score can be inferred from the chromosome itself by using fitness function. • Trait (Allele): Possible aspect (features) of an individual. • Genome: Collection of all chromosomes (traits) for an individual. • Genotype: The raw genetic information in the chromosome. • Phenotype: The expressive of the chromosome in terms of the model.

17. Genetic Algorithm Cycle

18. Flow chart of GA

19. Advantages of GA 1. Parallelism 2. Reliability 3. Solution space is wider 4. The fitness landscape is complex 5. Easy to discover global optimum 6. The problem has multi objective function 7. Only uses function evaluations. 8. Easily modified for different problems. 9. Handles noisy functions well. 10. Handles large, poorly understood search spaces easily

20. Advantages of GA 11. Good for multi-modal problems Returns a suite of solutions. 12. Very robust to difficulties in the evaluation of the objective function. 13. They require no knowledge or gradient information about the response surface 14. Discontinuities present on the response surface have little effect on overall optimization performance 15. They are resistant to becoming trapped in local optima 16. They perform very well for large-scale optimization problems 17. Can be employed for a wide variety of optimization problems

21. Limitations of GA 1. The problem of identifying fitness function 2. Definition of representation for the problem 3. Premature convergence occurs 4. The problem of choosing the various parameters like the size of the population, mutation rate, cross over rate, the selection method and its strength. 5. Cannot use gradients. 6. Cannot easily incorporate problem specific information 7. Not good at identifying local optima 8. No effective terminator. 9. Not effective for smooth unimodal functions 10. Needs to be coupled with a local search technique. 11. Have trouble finding the exact global optimum 12. Require large number of response (fitness) function evaluations 13. Configuration is not straightforward

22. premature convergence premature convergence means that a population for an optimization problem converged too early, resulting in being suboptimal. In this context, the parental solutions, through the aid of genetic operators, are not able to generate offsprings that are superior to their parents. Premature convergence can happen in case of loss of genetic variation (every individual in the population is identical)

23. Strategies for preventing premature convergence • A mating strategy called incest prevention. • Uniform crossover, • Favored replacement of similar individuals (preselection or crowding), • Segmentation of individuals of similar fitness (fitness sharing), • Increasing population size. • The genetic variation can also be regained by mutation though this process is highly random.

24. Biological Background“Cell” • Every animal cell is a complex of many small “factories” working together. • The nucleus in the centre of the cell. • The nucleus contains the genetic information.

25. Biological Background“Cell”

26. Biological Background “Chromosome” • Genetic Information is stored in the chromosomes. • Each chromosome is built of DNA. • Genes are encoded in the chromosomes. • Genes codes for proteins. • Every gene has a unique position on the chromosome.

27. Biological Background: Genotype and phenotype •The entire combination of genes is called genotype •A genotype leads to a phenotype (eye color, height, disease predisposition) •The phenotype is affected by changes to the underlying genetic code

28. Biological Background “Reproduction ” • Reproduction of genetical information • Mitosis • Meiosis • Mitosis is copying the same genetic information to new offspring: there is no exchange of information. • Mitosis is the normal way of growing of multicell structures, like organs.

29. Biological Background Reproduction • Meiosis is the basis of sexual reproduction • After meiotic division 2 gametes appear • In reproduction two gametes conjugate to a zygote which will become the new individual • Crossovers leads to new genotype

30. Mutations • In any copying process errors can occur, so single (point) mutations are pretty common. • Other types of errors, including affecting longer regions (either deletion, inversions, substitutions etc.) can also occur

31. “Natural selection” • The origin of species: “Preservation of favourable variations and rejection of unfavourable variations.” • There are more individuals born than can survive, so there is a continuous struggle for life. • Individuals with an advantage have a greater chance for survive: so survival of the fittest.

32. GA Steps over an Iteration process SELECTION: The first step consists in selecting individuals for reproduction. This selection is done randomly with a probability depending on the relative fitness of the individuals so that best ones are often chosen for reproduction than poor ones. REPRODUCTION: In the second step, offspring are bred by the selected individuals. For generating new chromosomes, the algorithm can use both recombination and mutation. EVALUATION: Then the fitness of the new chromosomes is evaluated. REPLACEMENT: During the last step, individuals from the old population are killed and replaced by the new ones.

33. Basic genetic algorithm • [start] Genetic random population of n chromosomes (suitable solutions for the problem) • [Fitness] Evaluate the fitness f(x) of each chromosome x in the population • [New population] Create a new population by repeating following steps until the New population is complete • Selection • Crossover • Mutation • Accepting • Replace • Test [ End condition] • Loop [Fitness]

34. Encoding Encoding is a process of representing individual genes. The process can be performed using bits, numbers, trees, arrays, lists or any other objects. The encoding depends mainly on solving the problem. Types of Encoding • Binary Encoding • Octal Encoding • Hexadecimal Encoding • Permutation Encoding (Real Number Coding) • Value Encoding • Tree Encoding

35. Binary Encoding • Octal Encoding

36. Hexadecimal Encoding • Permutation Encoding (Real Number Coding)

37. Value Encoding • Tree Encoding This Encoding mainly used for evolving program expression for genetic programming. Every chromosome is a tree of some objects such as function and commands of a programming language.

38. Breeding The breeding process is the heart of the genetic algorithm. It is in this process, the search process creates new and hopefully fitter individuals. The breeding cycle consists of three steps: a. Selecting parents. b. Crossing the parents to create new individuals (offspring or children). c. Replacing old individuals in the population with the new ones.

39. Selection

40. Selection • Selection is a method that randomly picks chromosomes out of the population according to their evaluation function. • The higher the fitness function, the more chance an individual has to be selected. • The selection pressure (Selection Intensity) is defined as the degree to which the better individuals are favored. • The higher the selection pressured, the more the better individuals are favored. • This selection pressure drives the GA to improve the population fitness over the successive generations. • Higher selection pressures resulting in higher convergence rates

41. Selection • if the selection pressure is too low, the convergence rate will be slow, and the GA will take unnecessarily longer time to find the optimal solution (Slow finishing). • If the selection pressure is too high, there is an increased change of the GA prematurely converging to an incorrect (sub-optimal) solution.

42. Selection methods • Tournament Selection • Truncation Selection • Linear Ranking Selection • Exponential Ranking Selection • Elitist Selection • Proportional Selection

43. Selection Schemes • Average Fitness • Fitness Variance • Reproduction Rate • Loss of Diversity • Selection Intensity • Selection Variance

44. Tournament Selection Tournament selection works as follows Choose some number “t” of individuals randomly from the population and copy the best individual from this group into the intermediate population and repeat N times Often tournaments are held only between two individuals binary tournament but a generalization is possible to an arbitrary group size “t” called tournament size

45. Truncation Selection In Truncation selection with threshold T only the fraction T best individuals can be selected and they all have the same selection probability This selection method is often used by breeders and in population genetic

46. Linear Ranking Selection Ranking selection was first suggested by Baker to eliminate the serious disadvantages of proportionate selection. For ranking selection the individuals are sorted according their fitness values and the rank N is assigned to the best individual and the rank to the worst individual. The selection probability is linearly assigned to the individuals according to their rank.

47. Exponential Ranking Selection • Exponential ranking selection differs from linear ranking selection in that the probabilities of the ranked individuals are exponentially weighted • The base of the exponent is the parameter 0< c < 1 of the method • The closer c is to 1 the lower is the exponentially of the selection method

48. Elitist Selection • The first best chromosome or the few best chromosomes are copied to the new population. • The rest is done in a classical way. Such individuals can be lost if they are not selected to reproduce or if crossover or mutation destroys them.

49. Proportional Selection • The probability of an individual to be selected is simply proportionate to its fitness value

50. Crossover (Recombination) • Crossover is the process of taking two parent solutions and producing from them a child. • After the selection (reproduction) process, the population is enriched with better individuals. • Reproduction makes clones of good strings but does not create new ones. • Crossover operator is applied to the mating pool with the hope that it creates a better offspring.