Evolutionary computation
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In computer science evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems.
Whereas evolutionary algorithms generally only involve techniques implementing mechanisms such as reproduction, mutation, recombination, natural selection and survival of the fittest, evolutionary computation can be loosely recognised by the following criteria:
- iterative progress, growth or development (see evolution)
- population based
- guided random search
- parallel processing
- often biologically inspired
This mostly involves 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
- artificial immune systems
- Learnable Evolution Model
Contents |
[edit] Related topics
[edit] 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]
[edit] Journals
- Evolutionary Computation
- IEEE Transactions on Evolutionary Computation
- Genetic Programming and Evolvable Machines
