Evolutionary Programming (EP)

Evolutionary programming, for short EP, (also sometimes called genetic algorithms(GA)) steals the idea of evolution from biology: sets of solutions are evolved by applying genetic operators such as crossover (a new solution is created by mating two parent solutions), and mutation (random change in a solution).
EP is getting more and more popular: 4 international conferences are scheduled for 1995 that center on EP topics.
EP technology is successfully applied in natural sciences and in engineering to complex problems, such as:
- prediction of chemical structures in 2D and 3D
- optimization in chemical engineering
- scheduling problems
- simulation of biological systems
EP techniques are also intensively used in machine learning research. Popular applications include:
- learning class desciptions from sets of examples
- genetic programming (learning programs through evolution)
- finding a good architectures for neural networks
- learning strategies in multi-agent environments