Methodology of interaction parent-child and m Markov

Type or paste a DOI name into the text box. The 2006 NASA ST5 spacecraft antenna. This complicated shape was found by an methodology of interaction parent-child and m Markov computer design program to create the best radiation pattern. It is known as an evolved antenna.

The evolution usually starts from a population of randomly generated individuals, and is an iterative process, with the population in each iteration called a generation. A standard representation of each candidate solution is as an array of bits. Arrays of other types and structures can be used in essentially the same way. Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of the mutation, crossover, inversion and selection operators. The population size depends on the nature of the problem, but typically contains several hundreds or thousands of possible solutions.

Occasionally, the solutions may be “seeded” in areas where optimal solutions are likely to be found. During each successive generation, a portion of the existing population is selected to breed a new generation. The fitness function is defined over the genetic representation and measures the quality of the represented solution. The fitness function is always problem dependent. For instance, in the knapsack problem one wants to maximize the total value of objects that can be put in a knapsack of some fixed capacity. For each new solution to be produced, a pair of “parent” solutions is selected for breeding from the pool selected previously. By producing a “child” solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its “parents”.