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.

History
In the fifties, the idea to use Darwinian principles for automated problem solving originated. 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 USA, 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.

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 optimization algorithms such as:
 * evolutionary algorithms (comprising genetic algorithms, evolutionary programming, evolution strategy, genetic programming and learning classifier systems)
 * swarm intelligence (comprising ant colony optimization and particle swarm optimization)

and in a lesser extent also:
 * self-organization such as self-organizing maps, growing neural gas, competitive learning demo applet
 * differential evolution
 * artificial life (also see digital organism)
 * cultural algorithms
 * harmony search algorithm
 * artificial immune systems
 * Learnable Evolution Model

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

 * Kalyanmoy Deb
 * David E. Goldberg
 * John Henry Holland
 * John Koza
 * Ingo Rechenberg
 * Hans-Paul Schwefel
 * Edward Tsang

Major conferences and workshops

 * The Genetic and Evolutionary Computation Conference (GECCO)
 * IEEE Congress on Evolutionary Computation (CEC)
 * Parallel Problem Solving from Nature (PPSN)
 * The Foundations of Genetic Algorithms workshop (FOGA)
 * The Workshop on Ant Colony optimization and Swarm Intellligence (ANTS)
 * The Evo* and EuroGP workshops

Journals

 * Evolutionary Computation
 * IEEE Transactions on Evolutionary Computation
 * Genetic Programming and Evolvable Machines