Presentation Summary 4. Introduction To Genetic Algorithms (GAs)
History of Genetic Algorithms
Darwins Theory of Evolution
Biological Background
Operation of Genetic Algorithm
Simple Example of Genetic Algorithms
Methodology associated with Genetic Algorithms
5. History Of Genetic Algorithms
Evolutionary Computing was introduced in the 1960s byI. Rechenberg .
John Holland wrote the first book on Genetic Algorithms Adaptation in Natural and Artificial Systems in 1975.
In 1992John Kozaused genetic algorithm to evolve programs to perform certain tasks. He called his method Genetic Programming .
6. Darwins Theory of Evolution
problems are solved by an evolutionary process resulting in a best (fittest) solution (survivor),
-In Other words, the solution is evolved
1. Inheritance Offspring acquire characteristics
2. Mutation Change, to avoid similarity
3. Natural Selection Variations improve survival
4. Recombination - Crossover
7. Biological Background
Chromosome
All Living organisms consists of cells. In each cell there is a same set of Chromosomes.
Chromosomes are strings of DNA and consists of genes, blocks of DNA.
Each gene encodes a trait, for example color of eyes.
Reproduction
During reproduction, recombination (or crossover) occurs first. Genes from parents combine to form a whole new chromosome. The newly created offspring can then be mutated. The changes are mainly caused by errors in copying genes from parents.
The fitness of an organism is measure by success of the organism in its life (survival)
8. Operation of Genetic Algorithms
Two important elements required for any problem before a genetic algorithm can be used for a solution are
Method for representing a solution
ex: string of bits, numbers, character
Method for measuring the quality of any proposed solution, using fitness function
ex: Determining total weight
Sequence of steps
1.Initialization
2.Selection
3.Reproduction
4.Termination
9. Initialization
Initially many individual solutions are randomly generated to form an initial population, covering the entire range of possible solutions (the search space)
Each point in the search space represents one possible solution marked by its value( fitness)
There are no of ways in which we would find a suitable solution and they dont provide the best solution. One way of finding solution from search space is Genetic Algorithms.
10. Selection
A proportion of the existing population is selected to bread a new bread of generation .
Reproduction
Generate a second generation population of solutions from those selected through genetic operators: crossover and mutation.
Termination
A solution is found that satisfies minimum criteria
The highest ranking solutions fitness is reaching or has reached
11. Simple Example for Genetic Algorithms
NP Complete problems
Problems in which it is very difficult to find solution, but once we have it, it is easy to check the solution.
Nobody knows if some faster algorithm exists to provide exact answers to NP-problems. An example of alternate method is the genetic algorithm.
Example: Traveling salesman problem.
12. Methodology Associated with GAs Begin Initialize population Optimum Solution? T=T+1 Selection Crossover Mutation N Evaluate Solutions Y Stop T =0 13. A Single Loop thru a Number of Evolving Populations
Simple_Genetic_Algorithm() { Initialize the Population; Calculate Fitness Function; While(Fitness Value != Optimal Value) { Selection ;//Natural Selection, Survival Of Fittest
14. Nature Vs Computer - Mapping Set of solutions. Solution to a problem. Quality of a solution. Encoding for a Solution. Part of the encoding of a solution. Crossover Population Individual Fitness Chromosome Gene Reproduction Computer Nature 15. Encoding Using String
Encoding of chromosomes is the first step in solving the problem and it depends entirely on the problem heavily
The process of representing the solution in the form of a string of bits that conveys the necessary information.
Just as in a chromosome, each gene controls a particular characteristic of the individual, similarly, each bit in the string represents a characteristic of the solution.
16. Encoding Methods
Binary Encoding Most common method of encoding. Chromosomes are strings of 1s and 0s and each position in the chromosome represents a particular characteristic of the problem.
11111110000000011111 Chromosome B 10110010110011100101 Chromosome A 17. Encoding Methods (contd.)
Permutation Encoding Useful in ordering problems such as the Traveling Salesman Problem (TSP). Example. In TSP, every chromosome is a string of numbers, each of which representsa city to be visited.
Value Encoding Used in problems where complicated values, such as real numbers, are used and where binary encoding would not suffice.
Good for some problems, butoften necessary to develop some specific crossover and mutation techniques for these chromosomes.
(left), (back), (left), (right), (forward) Chromosome B 1.235 5.323 0.454 2.321 2.454 Chromosome A 19. Encoding Methods(contd.) Tree Encoding This encoding is used mainly for evolving programs or expressions, i.e. for Genetic programming. In tree Encoding every chromosome is a tree of some objects, such as functions or commands in a programming language. In this example, we find a function that would approximate given pairs of values for a given input and output values. Chromosomes are functions represented in a tree. 20. Operation of Genetic Algorithms - Initialization - Selection - Reproduction - Termination 21. - Initially many individual solutions are randomly generated to forman initial population.- The population size depends on the nature of the problem,but typically contains several hundreds or thousands of possible solutions. - Traditionally, the population is generated randomly, covering theentire range of possible solutions (the search space).Initialization 22. There are many different techniques which a genetic algorithm can useto select the individuals to be copied over into the next generation (epoch). Listed are some of the most commonly used: . Roulette-Wheel Selection . Tournament Selection . Elitist Selection . Rank Selection . Hierarchical Selection Selection Methods 23.
A fitness function quantifies the optimality of a solution (chromosome) so that that particular solution may be ranked against all the other solutions
It depicts the closeness of a given solution to the desired result.
Watch out for its speed.
Most functions are stochastic and designed so that a small proportion of less fit solutions are selected. This helps keep the diversity of the population large, preventing premature convergence on poor solutions.
Fitness Function 24. Example Of Selection Example referred from Goldberg 89--www.cs.vu.nl/
