Genetic Algorithms

Limitations of Genetic Algorithm:

Though Genetic algorithms has proved to be a fast and powerful problem-solving approach, some limitations are found embedded in it. Some of these limitations are discussed below.
  1. The first, and most important, consideration in creating a genetic algorithm is defining a representation for the problem. The language used to specify candidate solutions must be robust; i.e., it must be able to tolerate random changes such that fatal errors do not result.


  2. One major obstacle of genetic algorithms is the coding of the fitness (evaluation) function so that a higher fitness can be attained and better solutions for the problem at hand are produced. A wrong choice of the fitness function may lead to critical problems such as unable to find the solution to a problem or even worse, returning a wrong solution to the problem.



  3. Along with making a good choice of fitness function, the other parameters of a Genetic Algorithm like population size, mutation and crossover rate must also be chosen with care. A small population size will not give the Genetic Algorithm enough solution space to produce accurate results. A high frequency of genetic change or poor selection scheme will result in disrupting the beneficial schema and the population may enter error catastrophe, changing too fast for selection to ever bring about convergence.


  4. It is not advisable to use Genetic algorithms for analytical problems. Though Genetic algorithms can find accurate solutions to these kind of problems, traditional analytic methods can find the same solutions in less time with few computational steps.


  5. Premature convergence is another issue GA scientists need to take into account when solutions of Generic algorithm are generated. It may happen that an individual can be much more fit than any of its competitors. So this individual may reproduce many more new individuals reducing the diversity of the new population and leading the algorithm to converge on the local optimum that represents that particular individual rather than searching the fitness landscape thoroughly enough to find the global optimum. This kind of inefficiency is mostly seen in small problems where even chance variations in reproduction rate may cause one genotype to become dominant over others.



Applications of Genetic Algorithms

Genetic algorithms in Robotics:

Robotics is one of the most talked about field in the computer industry today and it is used in wide range of industries in order to increase efficiency and accuracy. As the environment in which robots work change with time it becomes extremely difficult for programmers to figure out every possible behaviour of the robot in order to cope with the changes. It is here, where Genetic Algorithm plays a crucial role. Hence a suitable approach is needed which will lead the robot to its goal and will make it adapt to new situations as it encounters them. Genetic Alogrithms are adaptive search techniques which are used to learn high performance knowledge structures. Genetic algorithms make parallel search of the solution space via a population of candidate solutions and this population is manipulated in the simulation. The candidate solutions represent every possible behaviour of the robot and based on the overall performance of the candidates, each could be assigned a fitness value. Genetic operators are then used to improve the behaviour of the population.

Genetic algorithms in Financial Planning:

Models for tactical asset allocation and international equity strategies have been improved with the use of GAs. Genetic algorithms are very efficient for financial modelling applications as they are driven by pay-offs which can be used to improve the accuracy of predictions and return over the benchmark set. Moreover these methods are robust allowing a greater range of extensions and constraints which may not be accommodated in traditional methods. :: The robot images go here::

Genetic algorithms in Code Breaking:

Genetic algorithms can be used to search the a large range of solutions of cipher .

TSP and sequence scheduling

The Travelling Salesman Problem is a particular problem where GA has known to provide accurate results with high computational efficiency.