What is Genetic Algorithm (GA)

Stochastic (population-based) search technique, inspired in the natural selection process, used to find approximate solutions to complex optimization problems.

Evolutionary algorithm based on stochastic parallel search technique.

An optimization technique based on the principles of natural selection and genetics, used to find solutions to complex problems by evolving a population of potential solutions over multiple generations.

Is a biologically inspired search technique used to solve optimization problems.

Genetic algorithm is a nature-inspired metaheuristic technique. GA mimic Charles Darwin’s theory of natural evolution.

In the computer science field of artificial intelligence, genetic algorithm is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a meta-heuristic) is routinely used to generate useful solutions to optimization and search problems. 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. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics, and other fields.

Stochastic (population-based) search technique, inspired in the natural selection process and used to find approximate solutions to complex optimization problems.

An evolutionary algorithm that works with encoded population-based search imitating nature through the following mechanisms: selection, reproduction and mutation. The encoding consists of mapping each possible solution belongs to the search space to a symbol string, usually bits. Each encoded string is called chromosome. A population of chromosomes which is a sample of the search space is the base for the heuristic search.

An optimization and search technique based on the principles of Darwin’s theory of natural selection and Mendel’s work in genetics on inheritance: the stronger individuals are likely to survive in a competing environment. It allows a population composed of many individuals (possible solutions) to evolve under specified selection rules to a state that maximizes their suitability for the specific application.

It is an evolutionary algorithm used for solving various optimization problems.

A class of algorithms commonly used for training neural networks. The process is modeled after the methods by which biological DNA are combined or mutated to breed new individuals. The crossover technique, whereby DNA reproduces itself by joining portions of each parent’s DNA, is used to simulate a form of genetic-like breeding of alternative solutions. Representing the biological chromosomes found in DNA, genetic algorithms use arrays of data, representing various model solutions. Genetic algorithms are useful for multi-dimensional optimization problems in which the chromosome can encode the values for connections found in the artificial neural network.