Genetic algorithm

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File:ESA JAXA HUMIES Trajectory.png

ESA JAXA HUMIES Trajectory

Genetic algorithm

A genetic algorithm (GA) is a search heuristic that is inspired by 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, crossover, and selection.

History

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.

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

The population in a genetic algorithm is a set of candidate solutions. Each candidate solution, often called a chromosome, is typically represented as a string of binary digits.

Fitness Function

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.

Selection

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.

Crossover

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.

Mutation

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.
• They are less likely to get stuck in local optima compared to traditional optimization methods.

Disadvantages

• Genetic algorithms can be computationally expensive.
• They require careful tuning of parameters such as population size, mutation rate, and crossover rate.

See Also

• Evolutionary algorithm
• Simulated annealing
• Particle swarm optimization
• Ant colony optimization

Related Pages

• Natural selection
• Optimization (mathematics)
• Search algorithm
• Machine learning