computational fluid dynamics is used to determine the air resistance of a vehicle whose shape is encoded as the phenotype), or even interactive genetic algorithms are used. In some problems, it is hard or even impossible to define the fitness expression in these cases, a simulation may be used to determine the fitness function value of a phenotype (e.g. The fitness of the solution is the sum of values of all objects in the knapsack if the representation is valid, or 0 otherwise. Not every such representation is valid, as the size of objects may exceed the capacity of the knapsack. A representation of a solution might be an array of bits, where each bit represents a different object, and the value of the bit (0 or 1) represents whether or not the object is in the knapsack. 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. The fitness function is always problem dependent. The fitness function is defined over the genetic representation and measures the quality of the represented solution. Other methods rate only a random sample of the population, as the former process may be very time-consuming. Certain selection methods rate the fitness of each solution and preferentially select the best solutions. Individual solutions are selected through a fitness-based process, where fitter solutions (as measured by a fitness function) are typically more likely to be selected. Main article: Selection (genetic algorithm)ĭuring each successive generation, a portion of the existing population is selected to breed a new generation.
Occasionally, the solutions may be "seeded" in areas where optimal solutions are likely to be found. Often, the initial population is generated randomly, allowing the entire range of possible solutions (the search space). The population size depends on the nature of the problem, but typically contains several hundreds or thousands of possible solutions.
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. Tree-like representations are explored in genetic programming and graph-form representations are explored in evolutionary programming a mix of both linear chromosomes and trees is explored in gene expression programming. Variable length representations may also be used, but crossover implementation is more complex in this case. The main property that makes these genetic representations convenient is that their parts are easily aligned due to their fixed size, which facilitates simple crossover operations. Arrays of other types and structures can be used in essentially the same way. a fitness function to evaluate the solution domain.Ī standard representation of each candidate solution is as an array of bits (also called bit set or bit string).
The more fit individuals are stochastically selected from the current population, and each individual's genome is modified ( recombined and possibly randomly mutated) to form a new generation. In each generation, the fitness of every individual in the population is evaluated the fitness is usually the value of the objective function in the optimization problem being solved. 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. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.