A genetic algorithm is a search and optimisation technique inspired by evolutionary theory and genetics. In essence, a genetic algorithm is a problem-solving technique that uses genetic operations such as selection, crossover and mutation to generate a population of candidate solutions and evolve them over time to find an optimal solution.
In simpler terms, a genetic algorithm can be thought of as an optimisation technique that mimics biological evolution in which the candidate solutions are the "individuals" competing for survival and reproduction in a given environment. The algorithm uses genetic operations to generate a new generation of candidate solutions, which are evaluated in terms of their adaptation to the environment, and the process is repeated until an acceptable solution is found.
Evolutionary algorithms is a broader term that refers to a set of search and optimisation techniques based on biological evolution. Genetic algorithms are one of the most popular and widely used evolutionary algorithm techniques. However, there are other evolutionary algorithm techniques, such as evolutionary programming, evolutionary strategy and genetic programming, which differ in the genetic operations they use and how they evolve candidate solutions.
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