Introduction to Genetic Algorithms

— Genetic Algorithms (GAs) are adaptive methods that can be used to solve search and optimization problems. They are based on the genetic process of living organisms. Throughout the generations, the populations evolve in nature in accordance with the principles of natural selection and the survival of the strongest, postulated by Darwin. By imitating this process, Genetic Algorithms are able to create solutions for real world problems. The evolution of these solutions towards optimal values of the problem depends to a large extent on an adequate coding of them. A genetic algorithm consists of a mathematical function or a software routine that takes the specimens as inputs and returns as outputs which of them must generate offspring for the new generation. More complex versions of genetic algorithms generate an iterative cycle that directly takes the species and creates a new generation that replaces the old one a number of times determined by its own design. One of its main characteristics is that of perfecting its own heuristic in the execution process, so it does not require long periods of specialized training by the human being, the main defect of other methods to solve problems, such as Expert Systems.

Evolutionary algorithm (EA) is a generic population-based metaheuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the tness function determines the environment within which the solutions "live" . Evolution of the population then takes place after the repeated application of the above operators. This description is deliberately based on a unifying view resenting a general scheme that forms the common basis of all volutionary Algorithm variants. The main components of EAs are discussed, explaining their role and related issues of terminology. Further on we discuss general issues for EAs concerning their working. Finally, we put EAs into a broader context and explain their relation with other global optimisation techniques.

Genetic Algorithms (GAs) are a module of evolutionar y computing, which is a rapidly developing domain of artificial intelligence. These algorithms are inventive by Dar win's theor y about Dar winism. Naturally said, solution to a problem solved by GAs is evolved. In order to find an effective way to use GA widely, the basic knowledge of GA was introduced. After the introduction of its development, characteristic and application, the trends of its modification and application were analyzed. This algorithm is a optimization and search method for simulating natural choosing and genetics. This paper gives a brief introduction to genetic algorithms, its operators, and encoding techniques. This study has significance in theory of GA.

R C C h a k r a b o r t y , w w w . m y r e a d e r s . i n f o

1993

As quoted by Goldberg [1], Genetic algorithm (GA) is an adaptive optimization search algorithm aping the evolutionary ideas of natural selection. This Genetic Algorithm method is primarily applied haphazardly on an initial population and later all the individual chromosomes are appraised by a suitability function. This present paper is a review report on Genetic Algorithm and its functionaries on natural genetics and the evolutionary principle which was first proposed by Holland [2]. The GA is a simple but powerful tool for finding the global solution to an optimization problem. Genetic algorithms (GA), is a generally considered as an adaptive optimization search as like Darwinian natural selection [3] and genetics in biological systems. This methodology is a promising alternative to conventional heuristic methods. The Genetic algorithms functions with a set of candidate solutions named as population. As like the Darwinian principle of 'survival of the fittest', the Genetic algorithm obtains the ideal solution after a cycle of iterative computations. Genetic algorithms transact with large search spaces competently, on a solution to the problem until satisfactory results are acquired by stimulating successive populations of substitute solutions that are represented by a chromosome. Allied with the characteristics of utilization and investigation search, this algorithm has less chance to get local optimal solution than other algorithms. A fitness function considers the quality of a solution in the evaluation step. In this method, crossover and alteration functions are considered as the main operators that haphazardly effect the fitness value. Chromosomes are selected for reproduction by assessing the fitness value. The fitter chromosomes have greater probabilities to be nominated into the recombination pool using the roulette wheel or the tournament selection approaches.

1995

Abstract Genetic algorithms (GAs) are computer programs that mimic the processes of biological evolution in order to solve problems and to model evolutionary systems. In this paper I describe the appeal of using ideas from evolution to solve computational problems, give the elements of simple GAs, survey some application areas of GAs, and give a detailed example of how a GA was used on one particularly interesting problem—automatically discovering good strategies for playing the Prisoner's Dilemma.

The document covers the canonical or traditional genetic algorithm, the basic concepts, advantages. Few illustrations are given in detail. A variety of applications are also mentioned. Finally, the documents points to future directions and pointer to advanced features Genetic Algorithms are a family of computational models inspired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome-like data structure and apply recombination operators to these structures as as to preserve critical information. Genetic algorithms are often viewed as function optimizer, although the range of problems to which genetic algorithms have been applied are quite broad.