About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 40. Chapters: Chromosome (genetic algorithm), Clonal Selection Algorithm, Crossover (genetic algorithm), Cultural algorithm, Defining length, Edge recombination operator, Evolver (software), Fitness approximation, Fitness function, Fitness proportionate selection, Genetic algorithms in economics, Genetic algorithm scheduling, Genetic fuzzy systems, Genetic memory (computer science), Genetic operator, Genetic programming, Gene expression programming, Holland's schema theorem, HyperNEAT, Inheritance (genetic algorithm), List of genetic algorithm applications, Mutation (genetic algorithm), Neuroevolution of augmenting topologies, Parallel metaheuristic, Population-based incremental learning, Premature convergence, Promoter based genetic algorithm, Quality control and genetic algorithms, Reward-based selection, Santa Fe Trail problem, Schema (genetic algorithms), Search-based software engineering, Selection (genetic algorithm), Speciation (genetic algorithm), Stochastic universal sampling, Tournament selection, Truncation selection, Weasel program. Excerpt: In the computer science field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields. 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. 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. 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 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. 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
