Genetic algorithm: Difference between revisions

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Genetic Algorithm

A genetic algorithm (GA) is a search heuristic that is inspired by Charles Darwin's theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. Genetic algorithms are commonly used to generate high-quality solutions for optimization and search problems by relying on bio-inspired operators such as mutation, crossover, and selection.

Overview[edit]

Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.

Basic Concepts[edit]

Population: A set of candidate solutions to the optimization problem.
Chromosomes: A representation of a solution in the genetic algorithm. Typically, a chromosome is a string of binary numbers.
Genes: Parts of a chromosome, analogous to biological genes.
Fitness Function: A function that quantifies the optimality of a solution (chromosome) so that that particular solution may be ranked against all the other solutions.
Selection: The process of choosing the fittest individuals from the population to be parents for the next generation.
Crossover (Recombination): A genetic operator used to combine the genetic information of two parents to generate new offspring.
Mutation: A genetic operator used to maintain genetic diversity within a population by randomly tweaking the genes of a chromosome.

Process[edit]

1. Initialization: Generate an initial population of chromosomes randomly. 2. Evaluation: Calculate the fitness of each chromosome in the population. 3. Selection: Select the fittest chromosomes for reproduction. 4. Crossover: Perform crossover on the selected chromosomes to form a new offspring. 5. Mutation: Apply mutation to the offspring. 6. Replacement: Replace the least fit population with the new offspring. 7. Termination: Repeat the process until a termination condition is met, such as a maximum number of generations or a satisfactory fitness level.

Applications[edit]

Genetic algorithms are used in various fields, including:

Optimization: Solving complex optimization problems in engineering, economics, and logistics.
Machine Learning: Feature selection, hyperparameter tuning, and evolving neural networks.
Robotics: Path planning and control systems.
Bioinformatics: Sequence alignment and protein structure prediction.

Advantages and Disadvantages[edit]

Advantages[edit]

Robustness: GAs are robust and can handle noisy and complex search spaces.
Parallelism: They can explore multiple solutions simultaneously.
Flexibility: GAs can be applied to a wide range of problems.

Disadvantages[edit]

Computational Cost: GAs can be computationally expensive due to the large number of evaluations required.
Premature Convergence: They may converge prematurely to suboptimal solutions.

Also see[edit]

References[edit]

Goldberg, D. E. (1989). "Genetic Algorithms in Search, Optimization, and Machine Learning." Addison-Wesley.
Holland, J. H. (1975). "Adaptation in Natural and Artificial Systems." University of Michigan Press.

External links[edit]

[Genetic Algorithms on Scholarpedia](http://www.scholarpedia.org/article/Genetic_algorithms)

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-[[file:St_5-xband-antenna.jpg|thumb|St 5-xband-antenna]] [[file:ESA_JAXA_HUMIES_Trajectory.png|thumb|ESA JAXA HUMIES Trajectory|left]] '''Genetic algorithm'''
+Genetic Algorithm
-A '''genetic algorithm''' ('''GA''') is a [[search heuristic]] that is inspired by [[Charles Darwin|Charles Darwin's]] theory of [[natural selection]]. This algorithm reflects the process of natural evolution, where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. Genetic algorithms are commonly used to generate high-quality solutions for optimization and search problems by relying on bio-inspired operators such as [[mutation (genetic algorithm)|mutation]], [[crossover (genetic algorithm)|crossover]], and [[selection (genetic algorithm)|selection]].
+A genetic algorithm (GA) is a search heuristic that is inspired by Charles Darwin's theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. Genetic algorithms are commonly used to generate high-quality solutions for optimization and search problems by relying on bio-inspired operators such as mutation, crossover, and selection.
-== History ==
+== Overview ==
-The concept of genetic algorithms was introduced by [[John Holland]] in the 1960s at the [[University of Michigan]]. Holland's work was aimed at understanding the adaptive processes of natural systems and developing artificial systems that could mimic these processes.
+Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.
-== Basic Concepts ==
+=== Basic Concepts ===
-Genetic algorithms operate on a population of potential solutions applying the principle of survival of the fittest to produce better approximations to a solution. Each individual in the population represents a possible solution to the problem at hand.
-=== Population ===
+* '''Population''': A set of candidate solutions to the optimization problem.
-The population in a genetic algorithm is a set of candidate solutions. Each candidate solution, often called a [[chromosome (genetic algorithm)|chromosome]], is typically represented as a string of [[binary code|binary digits]].
+* '''Chromosomes''': A representation of a solution in the genetic algorithm. Typically, a chromosome is a string of binary numbers.
+* '''Genes''': Parts of a chromosome, analogous to biological genes.
+* '''Fitness Function''': A function that quantifies the optimality of a solution (chromosome) so that that particular solution may be ranked against all the other solutions.
+* '''Selection''': The process of choosing the fittest individuals from the population to be parents for the next generation.
+* '''Crossover (Recombination)''': A genetic operator used to combine the genetic information of two parents to generate new offspring.
+* '''Mutation''': A genetic operator used to maintain genetic diversity within a population by randomly tweaking the genes of a chromosome.
-=== Fitness Function ===
+=== Process ===
-The fitness function evaluates how close a given solution is to the optimum solution of the problem. The fitness function is problem-specific and is used to guide the evolution of the population.
+. '''Initialization''': Generate an initial population of chromosomes randomly.
+. '''Evaluation''': Calculate the fitness of each chromosome in the population.
+. '''Selection''': Select the fittest chromosomes for reproduction.
+. '''Crossover''': Perform crossover on the selected chromosomes to form a new offspring.
+. '''Mutation''': Apply mutation to the offspring.
+. '''Replacement''': Replace the least fit population with the new offspring.
+. '''Termination''': Repeat the process until a termination condition is met, such as a maximum number of generations or a satisfactory fitness level.
-=== Selection ===
+== Applications ==
-Selection is the process of choosing individuals from the population to create offspring for the next generation. The selection process is biased towards individuals with higher fitness scores.
+Genetic algorithms are used in various fields, including:
-=== Crossover ===
+* '''Optimization''': Solving complex optimization problems in engineering, economics, and logistics.
-Crossover, also known as recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. This operator is analogous to biological reproduction.
+* '''Machine Learning''': Feature selection, hyperparameter tuning, and evolving neural networks.
+* '''Robotics''': Path planning and control systems.
+* '''Bioinformatics''': Sequence alignment and protein structure prediction.
-=== Mutation ===
+== Advantages and Disadvantages ==
-Mutation is a genetic operator used to maintain genetic diversity within the population. It alters one or more gene values in a chromosome from its initial state.
-== Applications ==
-Genetic algorithms are used in various fields including [[engineering]], [[economics]], [[chemistry]], and [[bioinformatics]]. They are particularly useful for solving complex optimization problems where traditional methods are inefficient.
-== Advantages and Disadvantages ==
 === Advantages ===
-* Genetic algorithms are robust and can handle a wide range of optimization problems.
+* '''Robustness''': GAs are robust and can handle noisy and complex search spaces.
-* They are less likely to get stuck in local optima compared to traditional optimization methods.
+* '''Parallelism''': They can explore multiple solutions simultaneously.
+* '''Flexibility''': GAs can be applied to a wide range of problems.
 === Disadvantages ===
-* Genetic algorithms can be computationally expensive.
+* '''Computational Cost''': GAs can be computationally expensive due to the large number of evaluations required.
-* They require careful tuning of parameters such as population size, mutation rate, and crossover rate.
+* '''Premature Convergence''': They may converge prematurely to suboptimal solutions.
-== See Also ==
+== Also see ==
-* [[Evolutionary algorithm]]
+* [[Evolutionary_algorithm]]
-* [[Simulated annealing]]
+* [[Natural_selection]]
-* [[Particle swarm optimization]]
+* [[Optimization_problem]]
-* [[Ant colony optimization]]
+* [[Machine_learning]]
-== Related Pages ==
+== References ==
-* [[Natural selection]]
+* Goldberg, D. E. (1989). "Genetic Algorithms in Search, Optimization, and Machine Learning." Addison-Wesley.
-* [[Optimization (mathematics)]]
+* Holland, J. H. (1975). "Adaptation in Natural and Artificial Systems." University of Michigan Press.
-* [[Search algorithm]]
-* [[Machine learning]]
-[[Category:Genetic algorithms]]
+== External links ==
-[[Category:Optimization algorithms and methods]]
+* [Genetic Algorithms on Scholarpedia](http://www.scholarpedia.org/article/Genetic_algorithms)
+[[File:St_5-xband-antenna.jpg|thumb|An example of an application of genetic algorithms in antenna design.]]
+{{Evolutionary_algorithms}}
+[[Category:Optimization algorithms]]
 [[Category:Evolutionary algorithms]]
+[[Category:Machine learning]]
-{{Evolutionary algorithms}}
-{{Optimization algorithms}}
-{{Compu-bio-stub}}