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As soon as a food source is found, they bring food back to their nest, laying pheromones on the ground. The shortest path between the nest and the food source carries more pheromone than the rest paths, due to the more frequent passage of ants and pheromone evaporation. Thus, an ant starting its route from the nest will be strongly stimulated by higher pheromone levels and follow the shortest path to the food source, also laying its own pheromone on it. This is a simple yet efficient way of nature for solving shortest-path problems.

P. (1997). A history of evolutionary computation. In T. B. Fogel, & Z. ), Handbook of evolutionary computation (pp. 3:1-12). New York: IOP Press. De Jong, K. A. (1975). Analysis of behavior of a class of genetic adaptive systems. D. thesis, University of Michigan, USA. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Chichester, UK: John Wiley & Sons. Dixon, L. C. , & Szegö, G. P. (1978). The global optimization problem: an introduction. P. ), Towards global optimization 2 (pp.

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A descriptive Indonesian grammar by Isidore Dyen

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