Phd Thesis Evolutionary Algorithm

This iterative process may result in an improved adaptive-fit between the phenotypes of individuals in a population and the environment.The objective of the Genetic Algorithm is to maximize the payoff of candidate solutions in the population against a cost function from the problem domain.

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By using the Weibull distribution, the base year which is consistent with the percent probability of agricultural needs was determined for downstream of the Karun III dam.

To achieve the best cultivation pattern, initially the arable land was categorized into 6 classes and only 2100 hectares of agricultural irrigable land that had the best agricultural conditions were studied.

However, exceptionally, the accuracy of the GA algorithm was approximately 34% better than the HPSOGA algorithm for only the optimal storage capacity at Karun IV Dam.

The overall results show that the optimal values have higher importance in the preparation of the rule curve, especially in periods of drought.

The three dams are located in a consecutive series of Karun River in Iran.

In order to optimize, 41 years of the common statistical period were used.

The Genetic Algorithm is inspired by population genetics (including heredity and gene frequencies), and evolution at the population level, as well as the Mendelian understanding of the structure (such as chromosomes, genes, alleles) and mechanisms (such as recombination and mutation).

This is the so-called new or modern synthesis of evolutionary biology.

The optimization problem was modelled with the aim of maximizing the ultimate value of agriculture in terms of the number of acres of each crop.

The described model was resolved by linear programming and evolutionary algorithms in Microsoft Excel (Solver).

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