17-08-2013, 03:03 PM
Genetic Algorithms
Genetic Algorithms.ppt (Size: 193.5 KB / Downloads: 60)
Genetic Algorithms (GA) apply an evolutionary approach to inductive learning. GA has been successfully applied to problems that are difficult to solve using conventional techniques such as scheduling problems, traveling salesperson problem, network routing problems and financial marketing.
Digitalized Genetic knowledge representation
A common technique for representing genetic knowledge is to transform elements into binary strings.
For example, we can represent income range as a string of two bits for assigning “00” to 20-30k, “01” to 30-40k, and “11” to 50-60k.
Genetic operator - Crossover
The elements most often used for crossover are those destined to be eliminated from the population.
Crossover forms new elements for the population by combining parts of two elements currently in the population.
Genetic operator - Mutation
Mutation is sparingly applied to elements chosen for elimination.
Mutation can be applied by randomly flipping bits (or attribute values) within a single element.
Genetic operator - Selection
Selection is to replace to-be-deleted elements by copies of elements that pass the fitness test with high scores.
With selection, the overall fitness of the population is guaranteed to increase.
Step 1 of Supervised genetic learning
This step initializes a population P of elements. The P referred to population elements. The process modifies the elements of the population until a termination condition is satisfied, which might be all elements of the population meet some minimum criteria. An alternative is a fixed number of iterations of the learning process.
Step 2 of supervised genetic learning
Step 2a applies a fitness function to evaluate each element currently in the population. With each iteration, elements not satisfying the fitness criteria are eliminated from the population. The final result of a supervised genetic learning session is a set of population elements that best represents the training data.
Goal and condition
Our goal is to create a model able to differentiate individuals who have accepted the life insurance promotion from those who have not.
We require that after each iteration of the algorithm, exactly two elements from each class (life insurance promotion=yes) & (life insurance promotion=no) remain in the population.
Genetic Algorithms.ppt (Size: 193.5 KB / Downloads: 60)
Genetic Algorithms (GA) apply an evolutionary approach to inductive learning. GA has been successfully applied to problems that are difficult to solve using conventional techniques such as scheduling problems, traveling salesperson problem, network routing problems and financial marketing.
Digitalized Genetic knowledge representation
A common technique for representing genetic knowledge is to transform elements into binary strings.
For example, we can represent income range as a string of two bits for assigning “00” to 20-30k, “01” to 30-40k, and “11” to 50-60k.
Genetic operator - Crossover
The elements most often used for crossover are those destined to be eliminated from the population.
Crossover forms new elements for the population by combining parts of two elements currently in the population.
Genetic operator - Mutation
Mutation is sparingly applied to elements chosen for elimination.
Mutation can be applied by randomly flipping bits (or attribute values) within a single element.
Genetic operator - Selection
Selection is to replace to-be-deleted elements by copies of elements that pass the fitness test with high scores.
With selection, the overall fitness of the population is guaranteed to increase.
Step 1 of Supervised genetic learning
This step initializes a population P of elements. The P referred to population elements. The process modifies the elements of the population until a termination condition is satisfied, which might be all elements of the population meet some minimum criteria. An alternative is a fixed number of iterations of the learning process.
Step 2 of supervised genetic learning
Step 2a applies a fitness function to evaluate each element currently in the population. With each iteration, elements not satisfying the fitness criteria are eliminated from the population. The final result of a supervised genetic learning session is a set of population elements that best represents the training data.
Goal and condition
Our goal is to create a model able to differentiate individuals who have accepted the life insurance promotion from those who have not.
We require that after each iteration of the algorithm, exactly two elements from each class (life insurance promotion=yes) & (life insurance promotion=no) remain in the population.