What do you mean by Genetic Algorithm How it is different fr

What do you mean by Genetic Algorithm? How it is different from ‘classic’ search algorithms such as Steepest decent.

Solution

Genetic Algorithm:

Genetic Algorithms (GA) were developed by Prof. JohnHolland and his students at the University of Michigan during the 1960s and 1970s. Essentially, they are a method of \"breeding\" computer programs and solutions to optimization or search problems by means of simulated evolution. Processes loosely based on natural selection, crossover, and mutation are repeatedly applied to a population of binary strings which represent potential solutions. Over time, the number of above-average individuals increases, and highly-fit building blocks are combined from several fit individuals to find good solutions to the problem at hand.

It is better than conventional Artificial imminu in that it is more robust. Unlike older AI systems, they do not break easily even if the inputs changed slightly, or in the presence of reasonable noise. Also, in searching a large state-space, multi-modal state-space, or n-dimensional surface, a genetic algorithm may offer significant benefits over more typical search of optimization techniques. (linear programming, heuristic, depth-first, breath-first, and praxis.)

GAs are based on an analogy with the genetic structure and behaviour of chromosomes within a population of individuals using the following foundations:

How it is different from ‘classic’ search algorithms such as Steepest decent.

The steepest decent method is good for finding the minimum of a strictly convex functional. This is typical when you reformulate a nonlinear elliptic PDE as an optimization problem.

Genetic algorithm better suited for finding the global minimum of an objective functional which has many local minima, which can be the case for molecular systems that have lots of meta-stable equilibria. In that case, the objective function isn\'t convex everywhere, which precludes the use of steepest decent The cost is the slower convergence of randomized algorithms.