All Topics  
Evolutionary computation

 

 
   Email Print
   Bookmark   Link
 

 

Evolutionary computation
 
 
  Topics Evolutionary computation Topic Home

In computer science
Computer science

Computer science is the study of the theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems....
 evolutionary computation is a subfield of artificial intelligence
Artificial intelligence

Artificial intelligence is the intelligence of machines and the branch of computer science which aims to create it. Major AI textbooks define the field as "the study and design of intelligent agents,"...
 (more particularly computational intelligence
Computational intelligence

Computational intelligence is an offshoot of artificial intelligence. As an alternative to GOFAI it rather relies on heuristic algorithms such as in fuzzy systems, artificial neural network and evolutionary computation....
) that involves combinatorial optimization
Combinatorial optimization

Combinatorial optimization is a branch of Optimization . Its domain is optimization problems where the set of feasible solutions is Discrete set or can be reduced to a discrete one, and the goal is to find the best possible solution....
 problems.

Evolutionary computation uses iterative progress, such as growth or development in a population
Population

File:Population density.pngIn biology, a population is the collection of inter-breeding organisms of a particular species; in sociology, a collection of human beings....
. This population is then selected
Artificial selection

Artificial selection describes intentional breeding for certain traits, or combination of traits. It was defined by Charles Darwin in contrast to natural selection, in which the differential reproduction of organisms with certain traits is attributed to improved survival or reproductive ability ....
 in a guided random search using parallel processing
Parallel processing

Parallel processing is the ability of an entity to carry out multiple operations or tasks simultaneously. The term is used in the contexts of both human cognition and machine computation....
 to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution
Evolution

In biology, evolution is change in the heritability trait of a population of organisms from one generation to the next. These changes are caused by a combination of three main processes: variation, reproduction, and selection....
.

use of Darwinian
Darwinism

Darwinism is a term used for various movements or concepts related to ideas of transmutation of species or evolution, including ideas with no connection to the work of Charles Darwin....
 principles for automated problem solving originated in the fifties
1950s

The 1950s decade was the years of 1950 to 1959 inclusive. The Fifties in the developed western world are generally considered social conservative and highly Consumerism in nature....
.






Discussion
Ask a question about 'Evolutionary computation'
Start a new discussion about 'Evolutionary computation'
Answer questions from other users
Full Discussion Forum



Encyclopedia


In computer science
Computer science

Computer science is the study of the theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems....
 evolutionary computation is a subfield of artificial intelligence
Artificial intelligence

Artificial intelligence is the intelligence of machines and the branch of computer science which aims to create it. Major AI textbooks define the field as "the study and design of intelligent agents,"...
 (more particularly computational intelligence
Computational intelligence

Computational intelligence is an offshoot of artificial intelligence. As an alternative to GOFAI it rather relies on heuristic algorithms such as in fuzzy systems, artificial neural network and evolutionary computation....
) that involves combinatorial optimization
Combinatorial optimization

Combinatorial optimization is a branch of Optimization . Its domain is optimization problems where the set of feasible solutions is Discrete set or can be reduced to a discrete one, and the goal is to find the best possible solution....
 problems.

Evolutionary computation uses iterative progress, such as growth or development in a population
Population

File:Population density.pngIn biology, a population is the collection of inter-breeding organisms of a particular species; in sociology, a collection of human beings....
. This population is then selected
Artificial selection

Artificial selection describes intentional breeding for certain traits, or combination of traits. It was defined by Charles Darwin in contrast to natural selection, in which the differential reproduction of organisms with certain traits is attributed to improved survival or reproductive ability ....
 in a guided random search using parallel processing
Parallel processing

Parallel processing is the ability of an entity to carry out multiple operations or tasks simultaneously. The term is used in the contexts of both human cognition and machine computation....
 to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution
Evolution

In biology, evolution is change in the heritability trait of a population of organisms from one generation to the next. These changes are caused by a combination of three main processes: variation, reproduction, and selection....
.

History

The use of Darwinian
Darwinism

Darwinism is a term used for various movements or concepts related to ideas of transmutation of species or evolution, including ideas with no connection to the work of Charles Darwin....
 principles for automated problem solving originated in the fifties
1950s

The 1950s decade was the years of 1950 to 1959 inclusive. The Fifties in the developed western world are generally considered social conservative and highly Consumerism in nature....
. It was not until the sixties
1960s

The 1960s list of decades were the years from the start of 1960 to the end of 1969. The term also refers to an era more often called The Sixties, denoting the complex of inter-related cultural and political trends in the west, particularly United States, United Kingdom, France, Canada, Brazil, Australia, Spain, Italy, and Ger...
 that three distinct interpretations of this idea started to be developed in three different places.

Evolutionary programming
Evolutionary programming

Evolutionary programming is one of the four major evolutionary algorithm paradigms.It was first used by Lawrence J. Fogel in 1960 in order to use simulated evolution as a learning process aiming to generate artificial intelligence....
 was introduced by Lawrence J. Fogel
Lawrence J. Fogel

Dr. Lawrence J. Fogel , was a pioneer in evolutionary computation and human factors analysis. He is known as the father of evolutionary programming....
 in the USA
United States

The United States of America is a Federal government constitutional republic comprising U.S. state and a federal district. The country is situated mostly in central North America, where its Contiguous United States and Washington, D.C., the Capital districts and territories, lie between the Pacific Ocean and Atlantic Oceans, Borders of the U...
, while John Henry Holland
John Henry Holland

John Henry Holland is an American scientist and Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor....
 called his method a genetic algorithm
Genetic algorithm

A genetic algorithm is a Search algorithm wikt:technique used in computing to find exact or approximate solutions to Optimization and Search algorithm problems....
. In Germany
Germany

Germany , officially the Federal Republic of Germany , is a country in Central Europe. It is bordered to the north by the North Sea, Denmark, and the Baltic Sea; to the east by Poland and the Czech Republic; to the south by Austria and Switzerland; and to the west by France, Luxembourg, Belgium, and the Netherlands....
 Ingo Rechenberg
Ingo Rechenberg

Ingo Rechenberg is a Germany computer science and professor. Rechenberg is a pioneer of the fields of evolutionary computation and artificial evolution....
 and Hans-Paul Schwefel
Hans-Paul Schwefel

Hans-Paul Schwefel is a German computer scientist and professor emeritus at University of Dortmund , where he held the chair of systems analysis from 1985 until 2006....
 introduced evolution strategies
Evolution strategy

In computer science, evolution strategy is an optimization technique based on ideas of adaptation and evolution. It was created in the early 1960s and developed further along the 1970s and later by Ingo Rechenberg, Hans-Paul Schwefel and his co-workers, and belongs to the more general class of evolutionary computation or artificial evolutio...
. These areas developed separately for about 15 years. From the early nineties
1990s

The 1990s or Nineties was the decade that ran from January 1, 1990 to December 31, 1999. During this time, the widespread adoption of personal computers, the Internet, and the increased economic productivity led to the equity market booms around the world, and caused an influx of wealth to the United States, Canada, Europe, and Asia....
 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
Genetic programming

In artificial intelligence, genetic programming is an evolutionary algorithm-based methodology bio-inspired computing by biological evolution to find computer programs that perform a user-defined task....
.

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

Techniques

Evolutionary techniques mostly involve metaheuristic
Metaheuristic

A metaheuristic is a heuristic method for solving a very general class of computing problems by combining user-given procedural parameters ? usually heuristics themselves ? in the hope of obtaining a more efficient or more robust procedure....
 optimization
Optimization (computer science)

In computing, optimization is the process of modifying a system to make some aspect of it work more efficiently or use fewer resources. For instance, a computer program may be optimized so that it executes more rapidly, or is capable of operating with less Computer data storage or other resources, or draw less power....
 algorithm
Algorithm

In mathematics, computing, linguistics and related subjects, an algorithm is a sequence of finite instructions, often used for calculation and data processing....
s such as:
  • evolutionary algorithm
    Evolutionary algorithm

    In artificial intelligence, an evolutionary algorithm is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm....
    s (comprising genetic algorithms
    Genetic algorithm

    A genetic algorithm is a Search algorithm wikt:technique used in computing to find exact or approximate solutions to Optimization and Search algorithm problems....
    , evolutionary programming
    Evolutionary programming

    Evolutionary programming is one of the four major evolutionary algorithm paradigms.It was first used by Lawrence J. Fogel in 1960 in order to use simulated evolution as a learning process aiming to generate artificial intelligence....
    , evolution strategy
    Evolution strategy

    In computer science, evolution strategy is an optimization technique based on ideas of adaptation and evolution. It was created in the early 1960s and developed further along the 1970s and later by Ingo Rechenberg, Hans-Paul Schwefel and his co-workers, and belongs to the more general class of evolutionary computation or artificial evolutio...
    , genetic programming
    Genetic programming

    In artificial intelligence, genetic programming is an evolutionary algorithm-based methodology bio-inspired computing by biological evolution to find computer programs that perform a user-defined task....
     and learning classifier system
    Learning classifier system

    A learning classifier system, or LCS, is a machine learning system with close links to reinforcement learning and genetic algorithms. First described by John Henry Holland, his LCS consisted of a population of binary rules on which a genetic algorithm altered and selected the best rules....
    s)
  • swarm intelligence
    Swarm intelligence

    Swarm intelligence is a type of artificial intelligence based on the collective behavior of decentralization, Self organization systems. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of Cellular automaton systems....
     (comprising ant colony optimization
    Ant colony optimization

    The ant colony optimization algorithm , is a probability technique for solving computational problems which can be reduced to finding good paths through graph s....
     and particle swarm optimization
    Particle swarm optimization

    Particle swarm optimization is a swarm intelligence based algorithm to find a solution to an optimization problem in a search space, or model and predict social behavior in the presence of objectives....
    )


and in a lesser extent also:
  • self-organization
    Self-organization

    Self-organization is a process of attraction and VSEPR theory in which the internal organization of a system, normally an open system , increases in complexity without being guided or managed by an outside source....
     such as self-organizing map
    Self-organizing map

    A self-organizing map is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional , discretized representation of the input space of the training samples, called a map....
    s, growing neural gas
    Growing neural gas

    Growing Neural Gas is a self organization neural network first proposed by Bernd Fritzke. Unlike the earlier Neural Gas, Growing Neural Gas can add and delete nodes during algorithm execution....
    , competitive learning
  • differential evolution
    Differential evolution

    Differential Evolution is a method of mathematical Optimization of multidimensional functions and belongs to the class of evolution strategy optimizers....
  • artificial life
    Artificial life

    Artificial life is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry....
     (also see digital organism
    Digital organism

    A digital organism is a self-replication computer program that mutation and evolution . Digital organisms are used as a tool to study the dynamics of Darwinian evolution, and to test or verify specific hypotheses or mathematical models of evolution....
    )
  • cultural algorithm
    Cultural algorithm

    Cultural algorithms are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the population component....
    s
  • harmony search
    Harmony search

    Harmony search is a metaheuristic algorithm mimicking the improvisation process of musicians. In the process, each musician plays a note for finding a best harmony all together....
     algorithm
  • artificial immune system
    Artificial immune system

    In computer science, Artificial immune systems are computational systems inspired by the principles and processes of the vertebrate immune system....
    s
  • Learnable Evolution Model
    Learnable Evolution Model

    The Learnable Evolution Model is a novel, non-Darwinian methodology for evolutionary computation that employs machine learning to guide the generation of new individuals ....


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
Mutation

In biology, mutations are changes to the nucleotide sequence of the genetic material of an organism. Mutations can be caused by copying errors in the genetic material during cell division, by exposure to ultraviolet or ionizing radiation, chemical mutagens, or virus , or can be induced by the organism, itself, by cellular processes such as s...
, recombination
Recombination

Recombination may refer to:* Genetic recombination, the process by which genetic material is broken and joined to other genetic material* Carrier generation and recombination, processes by which mobile electrons and electron holes are created and eliminated...
, natural selection
Natural selection

Natural selection is the process by which favorable heritable trait become more common in successive generations of a population of Reproduction organisms, and unfavorable heritable traits become less common, due to differential reproduction of genotypes....
 and survival of the fittest
Survival of the fittest

"Survival of the fittest" is a phrase which is shorthand for a concept relating to competition for survival or predominance. Originally applied by Herbert Spencer in his Principles of Biology of 1864, Spencer drew parallels to his ideas of economics with Charles Darwin's theory of evolution by what Darwin termed natural selection....
. 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
Fitness function

A fitness function is a particular type of objective function that quantifies the optimality of a solution in a genetic algorithm so that that particular chromosome may be ranked against all the other chromosomes....
). Evolution
Evolution

In biology, evolution is change in the heritability trait of a population of organisms from one generation to the next. These changes are caused by a combination of three main processes: variation, reproduction, and selection....
 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
Stochastic

Stochastic means random.A stochastic process is one whose behavior is non-Deterministic system in that a system's subsequent state is determined both by the process's predictable actions and by a random element....
. 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

  • Kalyanmoy Deb
    Kalyanmoy deb

    Kalyanmoy Deb, is a professor of mechanical engineering at IIT Kanpur. His research lab is one of the premier places for evolutionary algorithms research in the world....
  • David E. Goldberg
    David E. Goldberg

    David Edward Goldberg is an American computer scientist, and professor at the department of Enterprise systems engineering at the University of Illinois at Urbana-Champaign and is most noted for his seminal works in the field of genetic algorithms....
  • John Henry Holland
    John Henry Holland

    John Henry Holland is an American scientist and Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor....
  • John Koza
    John Koza

    John R. Koza is a computer scientist and a consulting professor at Stanford University, most notable for his work in pioneering the use of genetic programming for the optimization of complex problems....
  • Ingo Rechenberg
    Ingo Rechenberg

    Ingo Rechenberg is a Germany computer science and professor. Rechenberg is a pioneer of the fields of evolutionary computation and artificial evolution....
  • Hans-Paul Schwefel
    Hans-Paul Schwefel

    Hans-Paul Schwefel is a German computer scientist and professor emeritus at University of Dortmund , where he held the chair of systems analysis from 1985 until 2006....
  • Edward Tsang
    Edward Tsang

    Edward Tsang is a Computer Science professor at the University of Essex. He holds a first degree in Business Administration from the Chinese University of Hong Kong , and an MSc and PhD in Computer Science from the University of Essex ....


Major conferences and workshops

  • The Genetic and Evolutionary Computation Conference (GECCO)
  • IEEE Congress on Evolutionary Computation
    IEEE Congress on Evolutionary Computation

    The IEEE Congress on Evolutionary Computation is one of the largest and most important conferences within Evolutionary computation , the other conferences of similar importance being Genetic and Evolutionary Computation Conference and Parallel Problem Solving from Nature ....
     (CEC)
  • Parallel Problem Solving from Nature (PPSN)
  • The Foundations of Genetic Algorithms workshop (FOGA)
  • The
  • The Evo* and EuroGP


Journals



See also

  • Estimation of distribution algorithm
  • Evolutionary robotics
    Evolutionary robotics

    Evolutionary Robotics is a methodology that uses evolutionary computation to develop controller for autonomous robots.Algorithms in ER frequently operate on populations of candidate controller ,...
  • Grammatical evolution
    Grammatical evolution

    Grammatical evolution is a relatively new evolutionary computation technique pioneered by Conor Ryan, JJ Collins and Michael O'Neill in 1998 at the in the ....
  • Human-based evolutionary computation
    Human-based evolutionary computation

    Human-based evolutionary computation is a set of evolutionary computation techniques that rely on human innovation. Human-based evolutionary computation techniques can be classified into three more specific classes analogous to ones in evolutionary computation....
  • Interactive evolutionary computation
    Interactive evolutionary computation

    Interactive evolutionary computation or Aesthetic Selection is a general term for methods of evolutionary computation that use human evaluation....
  • Mutation testing
  • No free lunch in search and optimization


Bibliography

  • 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.
  • 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. Bäck 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.


External links