Publication

Optimization of High Speed Synchronous Machines using Genetic Algorithms

2013
Student Projects

Abstract

Le but du projet est d’optimiser un moteur synchrone par des algorithmes génétiques. L’objectif de ce projet est de : • Etudier les algorithmes génétiques et ses applications. • Mettre en équation un moteur synchrone selon les différentes variables (électriques et mécaniques). • Combiner un algorithme génétique avec la mise en œuvre d’une machine. • Comparer les solutions.

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2013
Student Projects

Abstract

Official source

Related concepts (9)

Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

Memetic algorithm

A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence. Memetic algorithms represent one of the recent growing areas of research in evolutionary computation.

Chromosome (genetic algorithm)

In genetic algorithms (GA), or more general, evolutionary algorithms (EA), a chromosome (also sometimes called a genotype) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying to solve. The set of all solutions, also called individuals according to the biological model, is known as the population. The genome of an individual consists of one, more rarely of several, chromosomes and corresponds to the genetic representation of the task to be solved.

Related publications (2)

Optimization of High Speed Synchronous Machines using Genetic Algorithms

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