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Evolutionary computation

In computer science, evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that involves combinatorial optimization problems.

Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution.

As evolution can produce highly optimised processes and networks, it has many applications in computer science.



The use of Darwinian principles for automated problem solving originated in the fifties. It was not until the sixties that three distinct interpretations of this idea started to be developed in three different places.

Evolutionary programming was introduced by Lawrence J. Fogel in the US, while John Henry Holland called his method a genetic algorithm. In Germany Ingo Rechenberg and Hans-Paul Schwefel introduced evolution strategies. These areas developed separately for about 15 years. From the early nineties on they are unified as different representatives ( dialects ) of one technology, called evolutionary computing. Also in the early nineties, a fourth stream following the general ideas had emerged genetic programming. Since the 1990s, evolutionary computation has largely become swarm-based computation, and nature-inspired algorithms are becoming an increasingly significant part.

These terminologies denote the field of evolutionary computing and consider evolutionary programming, evolution strategies, genetic algorithms, and genetic programming as sub-areas.

Simulations of evolution using evolutionary algorithms and artificial life started with the work of Nils Aall Barricelli in the 1960s, and was extended by Alex Fraser, who published a series of papers on simulation of artificial selection.[1] Artificial evolution became a widely recognised optimisation method as a result of the work of Ingo Rechenberg in the 1960s and early 1970s, who used evolution strategies to solve complex engineering problems.[2] Genetic algorithms in particular became popular through the writing of John Holland.[3] As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs.[4] Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimise the design of systems.[5]


Evolutionary computing techniques mostly involve metaheuristic optimization algorithms. Broadly speaking, the field includes:

Evolutionary algorithms

  • Genetic algorithm
  • Genetic programming
  • Evolutionary programming
  • Evolution strategy
  • Differential evolution
  • Eagle strategy

Swarm intelligence

  • Ant colony optimization
  • Particle swarm optimization
  • Bees algorithm
  • Cuckoo search

and in a lesser extent also:

  • Artificial life (also see digital organism)
  • Artificial immune systems
  • Cultural algorithms
  • Firefly algorithm
  • Harmony search
  • Learning classifier systems
  • Learnable Evolution Model
  • Parallel simulated annealing
  • Self-organization such as self-organizing maps, competitive learning
  • Self-Organizing Migrating Genetic Algorithm
  • Swarm-based computing
  • Teaching-learning-based optimization (TLBO)

Evolutionary algorithms

Evolutionary algorithms form a subset of evolutionary computation in that they generally only involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solutions "live" (see also fitness function). Evolution of the population then takes place after the repeated application of the above operators.

In this process, there are two main forces that form the basis of evolutionary systems: Recombination and mutation create the necessary diversity and thereby facilitate novelty, while selection acts as a force increasing quality.

Many aspects of such an evolutionary process are stochastic. Changed pieces of information due to recombination and mutation are randomly chosen. On the other hand, selection operators can be either deterministic, or stochastic. In the latter case, individuals with a higher fitness have a higher chance to be selected than individuals with a lower fitness, but typically even the weak individuals have a chance to become a parent or to survive.

Evolutionary computation practitioners

Incomplete list:

  • Kalyanmoy Deb
  • David E. Goldberg
  • John Henry Holland
  • John Koza
  • Peter Nordin
  • Ingo Rechenberg
  • Hans-Paul Schwefel
  • Peter J. Fleming
  • Carlos M. Fonseca
  • Lee Graham
  • R. V. Rao

Major conferences and workshops

  • IEEE Congress on Evolutionary Computation (CEC)
  • Genetic and Evolutionary Computation Conference (GECCO)[6]
  • International Conference on Parallel Problem Solving From Nature (PPSN)[7]


MCMLL is a software suite containing a variety of evolutionary algorithms.

See also

  • Estimation of distribution algorithm
  • Evolutionary robotics
  • Fitness approximation
  • Grammatical evolution
  • Human-based evolutionary computation
  • Inferential programming
  • Interactive evolutionary computation
  • Mutation testing
  • No free lunch in search and optimization
  • Universal Darwinism


  • K. A. De Jong, Evolutionary computation: a unified approach. MIT Press, Cambridge MA, 2006
  • A. E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, 2003, ISBN 3-540-40184-9
  • A. E. Eiben and M. Schoenauer, Evolutionary computing, Information Processing Letters, 82(1): 1 6, 2002.
  • S. Cagnoni, et al, Real-World Applications of Evolutionary Computing, Springer-Verlag Lecture Notes in Computer Science, Berlin, 2000.
  • W. Banzhaf, P. Nordin, R.E. Keller, and F.D. Francone. Genetic Programming An Introduction. Morgan Kaufmann, 1998.
  • D. B. Fogel. Evolutionary Computation. Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ, 1995.
  • H.-P. Schwefel. Numerical Optimization of Computer Models. John Wiley & Sons, New-York, 1981. 1995 2nd edition.
  • Th. B ck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1):1 23, 1993.
  • J. R. Koza. Genetic Programming: On the Programming of Computers by means of Natural Evolution. MIT Press, Massachusetts, 1992.
  • D. E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989.
  • J. H. Holland. Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, 1975.
  • I. Rechenberg. Evolutionstrategie: Optimierung Technisher Systeme nach Prinzipien des Biologischen Evolution. Fromman-Hozlboog Verlag, Stuttgart, 1973.
  • L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. New York: John Wiley, 1966.


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