# genetic algorithm

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## genetic algorithm

n.
An algorithm that solves a problem using an evolutionary approach by generating mutations to the current solution method, selecting the better methods from this new generation, and then using these improved methods to repeat the process.
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The parameters of Jeager-Erdoes contained four unknown constants (A, B, K and D) were solved by Genetic Algorithm (GA) method under no assumption.
Compared with the traditional heuristic optimization search algorithm, the main characteristic of genetic algorithm is the population search strategy and the simple genetic operators.
Figure 7 shows the AE events localization maps of events related to the use of AMA algorithm (top) and genetic algorithm (bottom).
In this paper, we introduce the genetic algorithm (GA) as one of these metaheuristics and review some of its applications in medicine.
A brief description about Single-Depot Multiple Traveling Salesman Problem, SpaceFilling Curves and Genetic Algorithm is given in Sections III, IV and V, respectively.
A genetic algorithm for solving linear systems of equation is proposed in paper [42].
The design procedure of foundations for given loading, soil properties and strengths of structural material is framed in an optimisation process using genetic algorithm, in which the optimisation variables are the footing dimensions and depth and the objective function is the total construction cost, treating the design requirements as design constraints.
The technique of artificial intelligence by genetic algorithm has been employed in different areas seeking solutions for optimization problems (Miranda et al., 2015; Salvino et al., 2015).
A brief explanation of this architecture is that when a problem is very complex then the problem can be divided into subproblems (solved by simpler fuzzy systems) to study better each part of the problem and then all the outputs of the individual fuzzy systems (controllers) can go into an aggregation module that can appropriately join the outputs of all the individual fuzzy controller systems and all the systems are optimized using a conventional genetic algorithm obtaining new and better values for controlling the plant, and one of the tests with this architecture is illustrated in [17].
In genetic algorithm, after the initial generation population is generated by binary coding, the optimal solution is searched according to the natural evolution principle.

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