Phd Thesis Evolutionary Algorithm

Phd Thesis Evolutionary Algorithm-67
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.The amount of water allocated to the mentioned land was about 6.240 MCM.

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Listing (below) provides an example of the Genetic Algorithm implemented in the Ruby Programming Language.

The demonstration problem is a maximizing binary optimization problem called One Max that seeks a binary string of unity (all '1' bits).

Then, the optimal output of the problem in the form of curves that represent the desired amount of discharge from the reservoir at a specified time interval were prepared and compared with the Lingo model.

The regression analysis and artificial neural networks (ANN) were used to check the quality of the results.

The strategy for the Genetic Algorithm is to repeatedly employ surrogates for the recombination and mutation genetic mechanisms on the population of candidate solutions, where the cost function (also known as objective or fitness function) applied to a decoded representation of a candidate governs the probabilistic contributions a given candidate solution can make to the subsequent generation of candidate solutions.

Algorithm (below) provides a pseudocode listing of the Genetic Algorithm for minimizing a cost function.

The results showed full compliance of these two methods.

To estimate and predict the cost of the different stages of farming, and the cost of fertilizers needed for agricultural products, the obtained results of cultivation pattern per acre multiplied to cost breakdown values in tables taken from the ministry of agriculture.

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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