ALERT

[FREE DEMO] Deploy Your Enterprise Cloud in Minutes

Genetic Algorithm

Definition - What does Genetic Algorithm mean?

A genetic algorithm is a heuristic search method used in artificial intelligence and computing. It is used for finding optimized solutions to search problems based on the theory of natural selection and evolutionary biology. Genetic algorithms are excellent for searching through large and complex data sets. They are considered capable of finding reasonable solutions to complex issues as they are highly capable of solving unconstrained and constrained optimization issues.

Techopedia explains Genetic Algorithm

A genetic algorithm makes uses of techniques inspired from evolutionary biology such as selection, mutation, inheritance and recombination to solve a problem. The most commonly employed method in genetic algorithms is to create a group of individuals randomly from a given population. The individuals thus formed are evaluated with the help of the evaluation function provided by the programmer. Individuals are then provided with a score which indirectly highlights the fitness to the given situation. The best two individuals are then used to create one or more offspring, after which random mutations are done on the offspring. Depending on the needs of the application, the procedure continues until an acceptable solution is derived or until a certain number of generations have passed.

A genetic algorithm differs from a classical, derivative-based, optimization algorithm in two ways:

  • A genetic algorithm generates a population of points in each iteration, whereas a classical algorithm generates a single point at each iteration.
  • A genetic algorithm selects the next population by computation using random number generators, whereas a classical algorithm selects the next point by deterministic computation.

Compared to traditional artificial intelligence, a genetic algorithm provides many advantages. It is more robust and is susceptible to breakdowns due to slight changes in inputs or due to the presence of noise. With respect to other optimization methods like praxis, linear programming, heuristic, first or breadth-first, a genetic algorithm can provide better and more significant results while searching large multi-modal state spaces, large state spaces or n-dimensional surfaces.

Genetic algorithms are widely used in many fields such as robotics, automotive design, optimized telecommunications routing, engineering design and computer-aided molecular design.

Techopedia Deals

Connect with us

Techopedia on Linkedin
Techopedia on Linkedin
Tweat cdn.techopedia.com
"Techopedia" on Twitter


'@Techopedia'
Sign up for Techopedia's Free Newsletter!

Email Newsletter

Join thousands of others with our weekly newsletter

Resources
The 4th Era of IT Infrastructure: Superconverged Systems
The 4th Era of IT Infrastructure: Superconverged Systems:
Learn the benefits and limitations of the 3 generations of IT infrastructure – siloed, converged and hyperconverged – and discover how the 4th...
Approaches and Benefits of Network Virtualization
Approaches and Benefits of Network Virtualization:
Businesses today aspire to achieve a software-defined datacenter (SDDC) to enhance business agility and reduce operational complexity. However, the...
Free E-Book: Public Cloud Guide
Free E-Book: Public Cloud Guide:
This white paper is for leaders of Operations, Engineering, or Infrastructure teams who are creating or executing an IT roadmap.
Free Tool: Virtual Health Monitor
Free Tool: Virtual Health Monitor:
Virtual Health Monitor is a free virtualization monitoring and reporting tool for VMware, Hyper-V, RHEV, and XenServer environments.
Free 30 Day Trial – Turbonomic
Free 30 Day Trial – Turbonomic:
Turbonomic delivers an autonomic platform where virtual and cloud environments self-manage in real-time to assure application performance.