1812 Words8 Pages

1. Introduction

The most popular technique in evolutionary computation research has been the genetic algorithm. In the traditional genetic algorithm, the representation used is a fixed-length bit string. Each position in the string is assumed to represent a particular feature of an individual, and the value stored in that position represents how that feature is expressed in the solution. Usually, the string is “evaluated as a collection of structural features of a solution that have little or no interactions”. The analogy may be drawn directly to genes in biological organisms. Each gene represents an entity that is structurally independent of other genes. The main reproduction operator used is bit-string crossover, in which two strings are used as parents and new individuals are formed by swapping a*…show more content…*

Advantages and Limitations of Genetic Algorithms

The advantages of genetic algorithm includes:

1. Parallelism 2. Liability

3. Solution space is wider

4. The fitness landscape is complex

5. Easy to discover global optimum 6. The problem has multi objective function

7. Only uses function evaluations.

8. Easily modified for different problems.

9. Handles noisy functions well.

10. Handles large, poorly understood search spaces easily

11. Good for multi-modal problems Returns a suite of solutions.

12. Very robust to difficulties in the evaluation of the objective function.

The limitation of genetic algorithm includes:

1. The problem of identifying fitness function 2. Definition of representation for the problem 3. Premature convergence occurs 4. The problem of choosing the various parameters like the size of the population, mutation rate, cross over rate, the selection method and its strength.

5. Cannot use gradients.

6. Cannot easily incorporate problem specific information

7. Not good at identifying local optima

8. No effective terminator.

9. Not effective for smooth unimodal functions 10. Needs to be coupled with a local search

The most popular technique in evolutionary computation research has been the genetic algorithm. In the traditional genetic algorithm, the representation used is a fixed-length bit string. Each position in the string is assumed to represent a particular feature of an individual, and the value stored in that position represents how that feature is expressed in the solution. Usually, the string is “evaluated as a collection of structural features of a solution that have little or no interactions”. The analogy may be drawn directly to genes in biological organisms. Each gene represents an entity that is structurally independent of other genes. The main reproduction operator used is bit-string crossover, in which two strings are used as parents and new individuals are formed by swapping a

Advantages and Limitations of Genetic Algorithms

The advantages of genetic algorithm includes:

1. Parallelism 2. Liability

3. Solution space is wider

4. The fitness landscape is complex

5. Easy to discover global optimum 6. The problem has multi objective function

7. Only uses function evaluations.

8. Easily modified for different problems.

9. Handles noisy functions well.

10. Handles large, poorly understood search spaces easily

11. Good for multi-modal problems Returns a suite of solutions.

12. Very robust to difficulties in the evaluation of the objective function.

The limitation of genetic algorithm includes:

1. The problem of identifying fitness function 2. Definition of representation for the problem 3. Premature convergence occurs 4. The problem of choosing the various parameters like the size of the population, mutation rate, cross over rate, the selection method and its strength.

5. Cannot use gradients.

6. Cannot easily incorporate problem specific information

7. Not good at identifying local optima

8. No effective terminator.

9. Not effective for smooth unimodal functions 10. Needs to be coupled with a local search

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