Genetic algorithms

From Computer Science Wiki
HL CONTENT: Modeling & Simulation[1]

The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed integer programming, where some components are restricted to be integer-valued.[2]

Two videos to get you started[edit]


Use of genetic algorithms[edit]

The basic pattern of genetic algorithms[edit]

  1. A random set of solutions would be generated on the sample documents
  2. And tested against the human labelling
  3. Best fit solutions retained
  4. New generation created by mutating/crossing
  5. Algorithm repeated
  6. Until a good fit obtained

Terms associated with genetic algorithms[edit]

Helpful resources[edit]

Standards[edit]

  • Outline the use of genetic algorithms.

References[edit]