What is Evolutionary Computing

Transforms computers into automatic optimization and design tools, utilizing the power of the natural selection mechanisms: reproduction, mutation, Darwinian principle of survival of the fittest through inheritance, selection, and crossover. Various approaches to evolutionary computing include genetic algorithms (that mimic natural evolution to solve optimization and search problems), evolutionary programming (strategies using mutation as main variation operator, where a parent generates an offspring according to survivor selection principle), evolution strategies (using mutation and selection as search operators aimed at optimization techniques), and genetic programming (finding programs that would perform a user-defined task). Evolutionary computing resulted in a progress in quantum computing, search algorithms development, sorting, electronic design, evolvable hardware (containing hardware, artificial intelligence and autonomous systems that change their architecture and behavior in response to its environment), and advances in computer programming.

A computational intelligence technique inspired by the evolution of biological species in nature and utilise the underlying abstract concepts of Darwinian evolution: natural selection and survival of the fittest to perform a stochastic search for solutions to a problem.

Branch of artificial intelligence which mimics biological evolution and often applied to optimization problems.

Artificial intelligence methods that are inspired by principles of biological evolution.

Another name for genetic algorithms, an exploited search routine based upon biological evolution.

Evolutionary Computing is the study of computational systems which use ideas and get inspirations from natural evolution and other biological systems. These types of computing techniques are basically designed for evolution of characteristics inherited from one generation to another generation. They are computer based problem solving systems which incorporate computational models of evolutionary processes. EC provides four main methods namely Genetic Algorithms (GA), Evolutionary Strategies (ES), Evolutionary Programming (EP) and Genetic Programming (GP).

Branch of artificial intelligence which mimics biological evolution and often applied to optimization problems.

Branch of artificial intelligence which mimics biological evolution and often applied to optimization problems.