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(Aktu Btech) Application of Soft Computing Important Unit-5 Genetic Algorithm (GA)

In B.Tech AKTU Quantum Book, learn about the practical Application of Soft Computing. Learn about significant applications, frequently asked questions, and important tips for learning this cutting-edge technology. Unit-5 Genetic Algorithm (GA)

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Q1. What is Genetic Algorithm? Draw the general flow diagram of genetic algorithm. 

Ans. Genetic algorithm (GA): 

  • 1. The genetic algorithm is a natural selection-based method for solving both limited and unconstrained optimisation problems.
  • 2. A population of individual solutions is repeatedly modified by the genetic algorithm.
  • 3. At each phase, the genetic algorithm chooses at random individuals from the present population to be parents and uses them to generate children for the following generation.
  • 4. The population advances towards an optimal solution over subsequent generations.

Flow chart: The genetic algorithm uses three main types of rules at each step to create the next generation from the current population : 

  • a. Selection rule: Selection criteria determine which individuals, known as parents, contribute to the population in the next generation. 
  • b. Crossover rule: Crossover rules combine two parents to form children for the next generation. 
  • c. Mutation rule: Mutation rules apply random changes to individual parents to form children.
What is Genetic Algorithm? Draw the general flow diagram of genetic algorithm. Application of Soft Computing

Q2. Write short notes on procedures of GA. 


  • 1. Start: Generate random population of n chromosomes.
  • 2. Fitness: Evaluate the fitness f(x) of each chromosome in the population.
  • 3. New population: Create a new population by repeating following steps until the new population is complete. 
    • a. Selection: Select two parent chromosomes from a population according to their fitness.
    • b. Crossover: With a crossover probability, the parents will cross over to produce new offspring (children). If no crossover occurred, the offspring is a carbon copy of the parents. 
    • c. Mutation: With a mutation probability mutate new offspring at each locus (position in chromosome). 
    • d. Accepting: Place new offspring in the new population.
  • 4. Replace: Use new generated population for a further run of the algorithm. 
  • 5. Test: If the end condition is satisfied, stop, and return the best solution in current population.
  • 6. Go to step 2.

Q3. What is simulated annealing ? What is the structure of a simulated annealing algorithm ? 


  • 1. Stimulated annealing is a mathematics and modelling strategy used to identify a global optimisation in a specific function or problem.
  • 2. The simulated annealing algorithm evolved from the annealing process in metal work.
  • 3. Annealing is the process of heating and cooling a material in order to modify its physical properties due to changes in its internal structure.
  • 4. When the metal cools, its new structure solidifies, causing the metal to retain its newly acquired qualities. 
  • 5. To imitate this heating process, we keep a temperature variable in simulated annealing. We set it hot at first and then let it gradually cool as the algorithm runs.
  • 6. While this temperature variable is high, the algorithm will be allowed to accept solutions that are worse than our current solution more frequently.
  • 7. As the temperature drops, so does the likelihood of accepting inferior solutions, allowing the algorithm to gradually zero in on a region of the search space where a near-optimal solution can be located.
  • 8. This gradual ‘cooling’ process makes the simulated annealing algorithm effective at finding a close to optimum solution when dealing with large problems which contain numerous local optimums. 


  • 1. First we need to set the initial temperature and create a random initial Solution. 
  • 2. Then we begin looping until stop condition is met. Usually either the system has sufficiently cooled, or a good enough solution has been found. 
  • 3. Then we select a neighbour by making a small change to current solution. 
  • 4. We then decide whether to move to that neighbour solution. 
  • 5. Finally, we decrease the temperature and continue looping.  

Q4. Why mutation is done in genetic algorithm ? Explain types of mutation. 

Ans. Mutation is done in genetic algorithm because: 

  • 1. It preserves genetic variation from one generation to the next of a population of genetic algorithm chromosomes.
  • 2. Using mutation, GA can provide a better solution to the problem.

Types of mutation: 

  • 1. Bit string mutation: The mutation of bit strings occurs through bit flips at random positions.
Why mutation is done in genetic algorithm ? Explain types of mutation. Application of Soft Computing

The probability of a mutation of a bit is 1/l, where l is the length of the binary vector. Thus, a mutation rate of l per mutation and individual selected for mutation is reached. 

  • 2. Flip bit: This mutation operator inverts the bits in the given genome (i.e., if the genome bit is 1, it is changed to 0 and vice versa). 
  • 3. Boundary: This mutation operator replaces the genome at random with either the lower or upper bound. This is applicable to both integer and float Benes. 
  • 4. Non-uniform: The use of the non-uniform mutation operator increases the likelihood that the amount of mutation will be zero in the next generation. It prevents the population from becoming stagnant in the early phases of evolution. 
  • 5 Uniform: This operation replaces the value of the specified gene with a uniform random value chosen between the gene’s upper and lower boundaries. 
  • 6. Gaussian: This operation adds a unit Gaussian distributed random value to the selected gene. If the new gene value goes outside of the user-specified lower or higher boundaries for that gene, it is clipped.

Q5. What is the main function of crossover operation in genetic algorithm ?


  • 1. The basic operator of a genetic algorithm is crossover. The performance of the genetic algorithm is determined by the crossover operator.
  • 2. The type of crossover operator employed for a problem is determined by the encoding used.
  • 3. The fundamental concept of the crossover process is the exchange of genetic material between two parents beyond the crossover sites.

Function of crossover operation/operator in genetic algorithm: 

  • 1. The crossover operator’s primary job is to introduce diversity into the population.
  • 2. Specific crossover for a certain problem can boost the genetic algorithm’s performance.
  • 3. Cross over combines parental solutions in the intention of producing better answers.
  • 4. Crossover operators are essential for guaranteeing proper building block mixing. 
  • 5. Crossover is employed to keep exploitation and exploration in balance. The performance of genetic algorithms is determined by the exploitation and exploration strategies. Exploitation is the utilisation of current information to identify a better solution, whereas exploration is the investigation of new and unknown solutions in the exploration space. 

Q6. Explain the applications of G.A. in general life. 

Ans. Application of GA: 

  • 1. Optimization: Genetic Algorithms are most typically utilized in optimisation issues where we must maximize or reduce a certain objective function value while adhering to a set of restrictions. 
  • 2. Economics: GAs are also used to characterize economic models such as the cobweb model, game theory equilibrium resolution, asset pricing, and so on. 
  • 3. Neural networks: GAs are also used to train neural networks, particularly recurrent neural networks. 
  • 4. Parallelization: GAs have very good parallel capabilities and have proven to be very effective means of tackling specific issues, as well as being a valuable study area.
  • 5. Image processing: GAs are also employed in digital image processing (DIP) activities such as dense pixel matching. 
  • 6. Machine learning: Genetics based machine learning (GBML) is a nice area in machine learning. 
  • 7. Robot trajectory generation: GAs have been used to plan the path that a robot arm will take as it moves from one location to another. 
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