USING MICROBIAL GENETIC ALGORITHM TO SOLVE CARD SPLITTING PROBLEM. GENETIC ALGORITHMS USING MICROBIAL GENETIC ALGORITHM TO SOLVE CARD SPLITTING PROBLEM.

OVERVIEW 1 PROBLEM STATEMENT 2 GENETIC ALGORITHM 3 WORKING 4 MICROBIAL GA 5 PROBLEM 6 SOLUTION 7 CONCLUSION

THE PROBLEM STATEMENT We have 10 cards numbered 1 to 10 We have to divide them into two piles so that: The sum of the first pile is as close as possible to 36. And the product of all in the second pile is as close as possible to 360.

WHAT ARE GENETIC ALGORITHMS? A genetic algorithm is a search technique used in computing, to find true or approximate solutions to optimization and search problems Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. Genetic algorithms are implemented as a computer simulation in which a population of abstract representations of candidate solutions to an optimization problem evolves towards better solutions.

HOW IT WORKS? Traditionally, solutions are represented in binary as strings of 0s and 1s. The evolution usually starts from a population of randomly generated individuals, and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly mutated) to form a new population. The new population is then used in the next iteration of the algorithm.

MICROBIAL GA The basic operation of the Microbial GA training is as follows: Pick two genotypes at random Compare Scores (Fitness) to come up with a Winner and Loser Go along genotype, at each locus (Point) That is: With some probability (randomness), copy from Winner to Loser (overwrite) With some probability (randomness), mutate that locus of the Loser So only the Loser gets changed, which gives a version of Elitism for free, this ensures that the best in the breed remains in the population.

CAUTION!!! But there are some essential issues to be aware of, when playing with GAs: The genotype will be different for a different problem domain The Fitness function will be different for a different problem domain These two items must be developed again, whenever a new problem is specified.

1. The Geneotype Well, for this PROBLEM, the problem domain states that we have 10 cards. So, a two dimensional genes array, which is a 30*10 array is created. The 30 represents a population size of 30. It could be any size, but should be big enough to allow some dominant genes to form.

2. The Fitness Function Loop through the population member's genes If the current gene being looked at has a value of 0, the gene is for the sum pile (pile 0), so add to the running calculation If the current gene being looked at has a value of 1, the gene is for the product pile (pile 1), so add to the running calculation Calculate the overall error for this population member. If this member's geneotype has an overall error of 0.0, then the problem domain has been solved

CONCLUSION The problem of card splitting though trivial displays the depth of genetic algorithms and allows its application in a vast number of varied fields. From game theory to computational creativity GA have also been applied to solve the travelling salesman problem. It can be concluded that for a problem involving optimization, GA can be game changing.