Genetic Systems Programming

This chapter presented the biological motivation and fundamental aspects of evolutionary algorithms and its constituents, namely genetic algorithm, evolution strategies, evolutionary programming and genetic programming. Most popular variants of genetic programming are introduced. Important advantages of evolutionary computation while compared to classical optimization techniques are also discussed.

This chapter illustrated the importance of GP techniques for evolving intrusion detection systems. MEP outperforms LGP for three of the considered classes and LGP outperform MEP for two of the classes. MEP classification accuracy is grater than 95% for all considered classes and for three of them is greater than 99.75%. It is to be noted that for real time intrusion detection systems MEP and LGP would be the ideal candidates because of its simplified implementation.

In this chapter, we presented a novel approach to generate adaptive matching automata for non-sequential pattern set using genetic programming. we first defined some notation and necessary terminologies. Then, we formulated the problem of pattern matching and the impact that the traversal order of the patterns has on the process efficiency, when the patterns are ambiguous. We also gave some heuristics that allow the engineering of a relatively good traversal order. In the main part of the chapter, we described the evolutionary approach that permits the discovery of traversal orders using genetic programming for a given pattern set. For this purpose, we presented how the encoding of traversal orders is done and consequently how the decoding of an evolved traversal order into the corresponding adaptive pattern-matcher. We also developed the necessary genetic operators and showed how the fitness of evolved traversal orders is computed. We evaluated how sound is the obtained traversal. The optimisation was based on three main characteristics for matching automata, which are termination, code size and required matching time. Finally, we compared evolutionary adaptive matching automata, obtained for some universal benchmarks, to their counterparts that were designed using classic heuristics.

In this chapter, two possible approaches for evolving complex behaviors were discussed. In the first approach, the GP is used to explore possible hierarchy in the solution through implementing ADF and maintaining a subroutine library or using neural networks as primitive functions.

In the second approach, human programmer set the architecture of the robot controller and then the GP is used to evolve each module of this architecture. Two examples of architectures were discussed, the subsumption architecture and action selection architecture. Two experiments were presented to demonstrate this approach. The first used subsumption architecture to control a team of two robots with different capabilities to implement a cooperative behavior. The second experiment used action selection architecture to allow switching between the simpler behaviors that constitute the main behavior.