AI Assignment 2

Topics: Genetic algorithm, Crossover, Genetic algorithms Pages: 7 (1359 words) Published: January 25, 2015


UNIVERSITI TENAGA NASIONAL
ACADEMIC YEAR 2014/2015
ARTIFICIAL INTELLIGENCE
CSNB 234
ASSIGNMENT TWO

LECTURER : DR. ALICIA TANG YEE CHONG

Group Members:
1) DIVIYA A/P NADARAJAHSN093050
2) THIVIYA NADARAJSN089841
3) JESJEET SINGH SW089804
4) FAREESH SAILESH TIMBADIASW090964

QUESTION 1

(a) Genetic Algorithms were invented to mimic some of the processes observed in the evolutionary ideas of genetic and natural selection based on survival of the fittest. Explain each step of the Genetic Algorithm.

There are three main steps for genetic algorithm which is random initialization of population, evaluation of fitness function and generation of new population. 

In random initialization of population, the initial population is created randomly with even number of individuals. An individual is characterized by a fixed-length binary bit string, which is called a chromosome. In evaluation of fitness function all the individuals of the initially created population are evaluated by means of a fitness function . The fitness function is then used in the next step, to create a genetic pool. After evaluating the fitness of the individuals of the initial population, a new population is created. The creation of a new generation is performed basically in three stages, reproduction, crossover and mutation. The overall goal of this step is to obtain a new population with individuals which have high fitness values.

In reproduction stage, the individuals are selected among the population depending on their fitness values i.e. individuals with lower fitness values are eliminated, whilst the others with higher fitness values are copied to the next generation one or more times. The population after reproduction stage is called mating pool.

In crossover stage, a genetic crossover operator is applied to the mating pool to generate new individuals. Thus individuals of the mating pool are paired randomly, and genetic couples are obtained. There are many crossover operators can be used but the most basic crossover operator is the one-point crossover operator, in this case a crossover point in the string bits of the selected pair is randomly chosen, and the bits of the two parents are interchanged at this point. In two-point crossover operation, the two crossover points are selected in the binary strings of the pair under consideration and between these points the bits are swapped. This crossover process is similar to the mating process in a biological system, where parents pass segments of chromosomes to their offspring and thus offspring can outperform their parents if they get ‘good’ genes from both parents.

In a mutation process it introduces further changes to a bit string. This is required because if the population does not contain all the encoded information required to solve a specific problem, no amount of gene mixing can provide a satisfactory solution. By applying the mutation operator, it is possible to produce new chromosomes. This can be implemented in various ways, and the most common technique is to change a randomly chosen bit in the bit string of the individual to be mutated. Thus certain bit is changed from 1 into 0 or from 0 into 1.

(b) List and explain any TWO (2) Genetic Algorithm operators with examples. The 2 Generic Algorithm operators are Crossover and Mutation. Performance of Genetic Algorithm very depends on them. Type and implementation of operators depends on encoding and also on a problem.

Crossover
Crossover is a genetic operator that combines or mixing two chromosomes to produce a new chromosome. Main idea about crossover is that the new chromosome may be better than both of the two chromosomes. Crossover will occur during evolution according to a user definable crossover probability and crossover selects genes from parent’s chromosomes and it will create a new offspring. There are 5 crossovers such as one point, two point, uniform,...
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