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Genetic algorithm artificial intelligence presentation

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Tauhidul Khandaker

Genetic algorithms are a type of artificial intelligence search technique inspired by natural selection. They work by randomly generating an initial population of solutions, evaluating their fitness, then breeding new solutions through selection, crossover and mutation over many generations until an optimal solution is found. Some key steps include randomly initializing a population, determining fitness, selecting parents, performing crossover on parents to create new solutions, mutating new solutions, determining fitness of new population, and repeating until a stopping criteria is met such as a good enough solution being found. Genetic algorithms have been applied to many optimization and search problems across various domains.

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Genetic Algorithm In the field of artificial intelligence...
Presenters’ Tauhidul Khandaker ID : 011 111 010 Abu Sayed ID : 011 111 009
Search Techniques
Why Genetic Algorithm? Widely-used in business, science and engineering Optimization and Search Problems Scheduling and Timetabling
Basic Algorithm of Genetic Algorithm randomly initialize population(t) determine fitness of population(t) repeat select parents from population(t) perform crossover on parents creating population(t+1) perform mutation of population(t+1) determine fitness of population(t+1) until best individual is good enough
Outline of the Basic Genetic Algorithm 1. [Start] Generate random population of n chromosomes (suitable solutions for the problem) 2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population 3. [New population] Create a new population by repeating following steps until the new population is complete 1. [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected) 2. [Crossover] With a crossover probability crossover the parents to form a new children. If no crossover was performed, children is an exact copy of parents. 3. [Mutation] With a mutation probability mutate new children at each position in chromosomeNext Page To Continue……
Outline of the Basic Genetic Algorithm 4. [Replace] Use new generated population for a further run of algorithm 5. [Test] If the end condition is satisfied, stop, and return the best solution in current population 6. [Loop] Go to step 2
List of Genetic Algorithm applications 1. Airlines Revenue Management. 2. Wireless sensor/ad-hoc networks. 3. Audio watermark insertion/detection. 4. Protein folding and protein/ligand docking. 5. Pop music record producer. 6. Financial Mathematics. 7. Finding hardware bugs. 8. Game theory equilibrium resolution.
Initial Population We start with a population of n random strings. Suppose that length is = 10 and total n = 6 s1 = 1111010101 s2 = 0111000101 s3 = 1110110101 s4 = 0100010011 s5 = 1110111101 s6 = 0100110000
Fitness Function: f() s1 = 1111010101 f (s1) = 7 s2 = 0111000101 f (s2) = 5 s3 = 1110110101 f (s3) = 7 s4 = 0100010011 f (s4) = 4 s5 = 1110111101 f (s5) = 8 s6 = 0100110000 f (s6) = 3 Total = 34
Selection (1) Next we apply fitness proportionate selection with the roulette wheel method: We repeat the extraction as many times as the number of individuals we need to have the same parent population size (6 in our case)
Selection (2) Suppose that, after performing selection, we get the following population: s1’ = 1111010101 (s1) s2’ = 1110110101 (s3) s3’ = 1110111101 (s5) s4’ = 0111000101 (s2) s5’ = 0100010011 (s4)
Basic Algorithm of Genetic Algorithm randomly initialize population(t) determine fitness of population(t) repeat select parents from population(t) perform crossover on parents creating population(t+1) perform mutation of population(t+1) determine fitness of population(t+1) until best individual is good enough
Perform Crossover Before Crossover S1’ = 1111010101 S2’ = 1110110101 S5’ = 0100010011 S6’ = 1110111101 After Crossover S1’ = 1110110101 S2’ = 1111010101 S5” = 0100011101 S6” = 1110110011
Basic Algorithm of Genetic Algorithm randomly initialize population(t) determine fitness of population(t) repeat select parents from population(t) perform crossover on parents creating population(t+1) perform mutation of population(t+1) determine fitness of population(t+1) until best individual is good enough
Mutation Before applying mutation: After applying mutation: s1” = 1110110101 s2” = 1111010101 s3” = 1110111101 s4” = 0111000101 s5” = 0100011101 s6” = 1110110011 s1”’ = 1110100101 s2”’ = 1111110100 s3”’ = 1110101111 s4”’ = 0111000101 s5”’ = 0100011101 s6”’ = 1110110001
Basic Algorithm of Genetic Algorithm randomly initialize population(t) determine fitness of population(t) repeat select parents from population(t) perform crossover on parents creating population(t+1) perform mutation of population(t+1) determine fitness of population(t+1) until best individual is good enough
Fitness of New Population After Applying Mutation s1”’ = 1110100101 s2”’ = 1111110100 s3”’ = 1110101111 s4”’ = 0111000101 s5”’ = 0100011101 s6”’ = 1110110001 f(s1”’) = 6 f(s2”’) = 7 f(s3”’) = 8 f(s4”’) = 5 f(s5”’) = 5 f(s6”’) = 6 Total = 37
Example (End) ➔ In one generation, the total population fitness changed from 34 to 37, thus improved by ~9%. ➔ At this point, we go through the same process all over again, until a stopping criteria is met.
Issues Choosing basic implementation issues: representation population size, mutation rate, ... selection, deletion policies crossover, mutation operators Termination Criteria Performance, scalability Solution is only as good as the evaluation function
When to Use a GA Alternate solutions are too slow or overly complicated Need an exploratory tool to examine new approaches Problem is similar to one that has already been successfully solved by using a GA Want to hybridize with an existing solution
Conclusion Inspired from Nature Has many areas of Applications GA is powerful
Thank You

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Genetic algorithm artificial intelligence presentation

  • 1. Genetic Algorithm In the field of artificial intelligence...
  • 2. Presenters’ Tauhidul Khandaker ID : 011 111 010 Abu Sayed ID : 011 111 009
  • 4. Why Genetic Algorithm? Widely-used in business, science and engineering Optimization and Search Problems Scheduling and Timetabling
  • 5. Basic Algorithm of Genetic Algorithm randomly initialize population(t) determine fitness of population(t) repeat select parents from population(t) perform crossover on parents creating population(t+1) perform mutation of population(t+1) determine fitness of population(t+1) until best individual is good enough
  • 6. Outline of the Basic Genetic Algorithm 1. [Start] Generate random population of n chromosomes (suitable solutions for the problem) 2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population 3. [New population] Create a new population by repeating following steps until the new population is complete 1. [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected) 2. [Crossover] With a crossover probability crossover the parents to form a new children. If no crossover was performed, children is an exact copy of parents. 3. [Mutation] With a mutation probability mutate new children at each position in chromosomeNext Page To Continue……
  • 7. Outline of the Basic Genetic Algorithm 4. [Replace] Use new generated population for a further run of algorithm 5. [Test] If the end condition is satisfied, stop, and return the best solution in current population 6. [Loop] Go to step 2
  • 8. List of Genetic Algorithm applications 1. Airlines Revenue Management. 2. Wireless sensor/ad-hoc networks. 3. Audio watermark insertion/detection. 4. Protein folding and protein/ligand docking. 5. Pop music record producer. 6. Financial Mathematics. 7. Finding hardware bugs. 8. Game theory equilibrium resolution.
  • 9. Initial Population We start with a population of n random strings. Suppose that length is = 10 and total n = 6 s1 = 1111010101 s2 = 0111000101 s3 = 1110110101 s4 = 0100010011 s5 = 1110111101 s6 = 0100110000
  • 10. Fitness Function: f() s1 = 1111010101 f (s1) = 7 s2 = 0111000101 f (s2) = 5 s3 = 1110110101 f (s3) = 7 s4 = 0100010011 f (s4) = 4 s5 = 1110111101 f (s5) = 8 s6 = 0100110000 f (s6) = 3 Total = 34
  • 11. Selection (1) Next we apply fitness proportionate selection with the roulette wheel method: We repeat the extraction as many times as the number of individuals we need to have the same parent population size (6 in our case)
  • 12. Selection (2) Suppose that, after performing selection, we get the following population: s1’ = 1111010101 (s1) s2’ = 1110110101 (s3) s3’ = 1110111101 (s5) s4’ = 0111000101 (s2) s5’ = 0100010011 (s4)
  • 13. Basic Algorithm of Genetic Algorithm randomly initialize population(t) determine fitness of population(t) repeat select parents from population(t) perform crossover on parents creating population(t+1) perform mutation of population(t+1) determine fitness of population(t+1) until best individual is good enough
  • 14. Perform Crossover Before Crossover S1’ = 1111010101 S2’ = 1110110101 S5’ = 0100010011 S6’ = 1110111101 After Crossover S1’ = 1110110101 S2’ = 1111010101 S5” = 0100011101 S6” = 1110110011
  • 15. Basic Algorithm of Genetic Algorithm randomly initialize population(t) determine fitness of population(t) repeat select parents from population(t) perform crossover on parents creating population(t+1) perform mutation of population(t+1) determine fitness of population(t+1) until best individual is good enough
  • 16. Mutation Before applying mutation: After applying mutation: s1” = 1110110101 s2” = 1111010101 s3” = 1110111101 s4” = 0111000101 s5” = 0100011101 s6” = 1110110011 s1”’ = 1110100101 s2”’ = 1111110100 s3”’ = 1110101111 s4”’ = 0111000101 s5”’ = 0100011101 s6”’ = 1110110001
  • 17. Basic Algorithm of Genetic Algorithm randomly initialize population(t) determine fitness of population(t) repeat select parents from population(t) perform crossover on parents creating population(t+1) perform mutation of population(t+1) determine fitness of population(t+1) until best individual is good enough
  • 18. Fitness of New Population After Applying Mutation s1”’ = 1110100101 s2”’ = 1111110100 s3”’ = 1110101111 s4”’ = 0111000101 s5”’ = 0100011101 s6”’ = 1110110001 f(s1”’) = 6 f(s2”’) = 7 f(s3”’) = 8 f(s4”’) = 5 f(s5”’) = 5 f(s6”’) = 6 Total = 37
  • 19. Example (End) ➔ In one generation, the total population fitness changed from 34 to 37, thus improved by ~9%. ➔ At this point, we go through the same process all over again, until a stopping criteria is met.
  • 20. Issues Choosing basic implementation issues: representation population size, mutation rate, ... selection, deletion policies crossover, mutation operators Termination Criteria Performance, scalability Solution is only as good as the evaluation function
  • 21. When to Use a GA Alternate solutions are too slow or overly complicated Need an exploratory tool to examine new approaches Problem is similar to one that has already been successfully solved by using a GA Want to hybridize with an existing solution
  • 22. Conclusion Inspired from Nature Has many areas of Applications GA is powerful