Computational Intelligence Computational Intelligence is a modern name for the sub-field of AI concerned with sub-symbolic also called messy, scruffy, and soft techniques. Computational Intelligence describes techniques that focus on strategy and outcome.
Optimization problems[ edit ] In a genetic algorithm, a population of candidate solutions called individuals, creatures, or phenotypes to an optimization problem is evolved toward better solutions. 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 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 new generation of candidate solutions is then used in the next iteration of the algorithm.
Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. A typical genetic algorithm requires: A standard representation of each candidate solution is as an array of bits.
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.
Variable length representations may also be used, but crossover implementation is more complex in this case. 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.
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.
Initialization[ edit ] The population size depends on the nature of the problem, but typically contains several hundreds or thousands of possible solutions. Often, the initial population is generated randomly, allowing the entire range of possible solutions the search space.
Occasionally, the solutions may be "seeded" in areas where optimal solutions are likely to be found. Selection genetic algorithm During each successive generation, a portion of the existing population is selected to breed a new generation. 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.
Certain selection methods rate the fitness of each solution and preferentially select the best solutions. Other methods rate only a random sample of the population, as the former process may be very time-consuming. 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. 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.
Not every such representation is valid, as the size of objects may exceed the capacity of the knapsack. The fitness of the solution is the sum of values of all objects in the knapsack if the representation is valid, or 0 otherwise.In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization.
The overview and experiments of PSO, ACS, and ABC are given.
Enhanced versions of these are also introduced. Preface. This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. Preface. This is the preprint of an invited Deep Learning (DL) overview.
One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal.
Particle Swarm Optimization. Particle Swarm Optimization, PSO. Taxonomy. Particle Swarm Optimization belongs to the field of Swarm Intelligence and Collective Intelligence and is a sub-field of Computational Intelligence. Dear Twitpic Community - thank you for all the wonderful photos you have taken over the years.
We have now placed Twitpic in an archived state. Overview of Algorithms for Swarm Intelligence - Essay Example Tagged Robot Typical swarm Intelligence schemes Include Particle Swarm Optimization SO), Ant Colony System (ACS), Stochastic Diffusion Search (SD), Bacteria Foraging (BE), the Artificial Bee Colony (BBC), and so on.