Soft Computing pp 363-381 | Cite as

Genetic Algorithms

Part of the Studies in Computational Intelligence book series (SCI, volume 103)

Origin with a protozoa (prime unicell animal) to existence of human (most developed living being) in nature as a result of evolution, is the main theme, adopted by genetic algorithms (GA), one of the most modern paradigm for general problem solving. Since the paradigm simulates the strategy of evolution, it is surprisingly simple but powerful, domain free approach to problem solving. GAs are gaining popularity in many engineering and scientific applications due to their enormous advantages such as adaptability, ability to handle non-linear, ill defined and probabilistic problems. As the approach is domain free, it has wide scope of applications and most of the optimization problems can be handled successfully with this approach.

The emergence of massively parallel computers made these algorithms of practical interest. There are various well known programs in this class like evolutionary programs, genetic algorithms, simulated annealing, classifier systems, expert systems, artificial neural networks and fuzzy systems. This chapter discusses a genetic algorithm – which is based on the principle of evolution (survival of fittest). In such algorithms a population of individuals (potential solution) undergoes a sequence of transformations like mutation type and crossover type. These individuals strive for survival; a selection scheme, biased towards fitter individuals, selects the next generation. After some number of generations the program converges to the optimal value.


Genetic Algorithm Genetic Programming Hill Climbing Simple Genetic Algorithm Parallel Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2008

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