Genetic algorithm in artificial intelligence by Code Venue

 

Genetic algorithm

• Evolutionary pace of learning algorithm is genetic algorithm
• The higher degree euguency can be achieved with new paradigm of AI called genetic algorithm
• Genetic algorithm is a rich flavour of stochastic beam search
• In the genetic algorithm two parent states are combined by which a good successor state will be generated
• In genetic algorithm, we are dealing with sexual and asexual reproduction

Mechanisms used in genetic algorithm:

1. Population
2. Individual
3. Fitness function
4. Crossover
5. Mutation
6. Schema

Population:

Population is a set of states which are generated randomly

Individual:

It is a state or individual and it is represented as string over a finite alphabets

Example

          A string of 0'sand 1's

Fitness function:

• It is evaluation function
• A fitness function should return higher values for better states
• In 8-queen the fitness function has 28 value for number of non attacking pairs

Crossover:

• Selection of state is dependent on fitness function
• If fitness function value is above limit then only state is selected Otherwise discarded
• Each pair or state is divided
• The division point or meeting point called as crossover point

Mutation:

• Mutation is one of the genetic operator
• Mutation works on random selection or changes
• The pattern switching 0 to 1 or 1 to #

Schema:

Schema is a substring

Position of some bit can be unspecified

Example:

8-Queen Problem

Working of genetic algorithm:

Input

1. State population
2. Fitness function

• Create an individual X by using selection with fitness function A
• Create an individual why by using selection with fitness function B
• Child with good fitness is created for X + Y