Genetic Algorithm – Short note on Implementation

Genetic algorithms are typically implemented as a computer simulation in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but different encodings are also possible. The evolution starts from a population of completely random individuals and happens in generations. In each generation, the fitness of the whole population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), modified (mutated or recombined) to form a new population, which becomes current in the next iteration of the algorithm.^†

References

†Genetic algorithm - Wikipedia, the free encyclopedia, last modified 00:36, 15 February 2006, http://en.wikipedia.org/wiki/ Genetic_algorithm

Related Posts:

• ABSTRACT
• INTRODUCTION
• JOB SHOP SCHEDULING
• Search Methods and Optimization Techniques
• Some other Optimisation methods

Genetic Algorithm

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