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About this book

Designing complex programs such as operating systems, compilers, filing systems, data base systems, etc. is an old ever lasting research area. Genetic programming is a relatively new promising and growing research area. Among other uses, it provides efficient tools to deal with hard problems by evolving creative and competitive solutions. Systems Programming is generally strewn with such hard problems. This book is devoted to reporting innovative and significant progress about the contribution of genetic programming in systems programming. The contributions of this book clearly demonstrate that genetic programming is very effective in solving hard and yet-open problems in systems programming. Followed by an introductory chapter, in the remaining contributed chapters, the reader can easily learn about systems where genetic programming can be applied successfully. These include but are not limited to, information security systems, compilers, data mining systems, stock market prediction systems, robots and automatic programming.

Table of Contents


1. Evolutionary Computation: from Genetic Algorithms to Genetic Programming

1.7 Summary
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.
Ajith Abraham, Nadia Nedjah, Luiza de Macedo Mourelle

2. Automatically Defined Functions in Gene Expression Programming

Without Abstract
Cândida Ferreira

3. Evolving Intrusion Detection Systems

3.7 Conclusions
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.
Ajith Abraham, Crina Grosan

4. Evolutionary Pattern Matching Using Genetic Programming

4.7 Summary
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.
Nadia Nedjah, Luiza de Macedo Mourelle

5. Genetic Programming in Data Modelling

Without Abstract
Halina Kwasnicka, Ewa Szpunar-Huk

6. Stock Market Modeling Using Genetic Programming Ensembles

Without Abstract
Crina Grosan, Ajith Abraham

7. Evolutionary Digital Circuit Design Using Genetic Programming

Without Abstract
Nadia Nedjah, Luiza de Macedo Mourelle

8. Evolving Complex Robotic Behaviors Using Genetic Programming

8.5 Summary
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.
Michael Botros

9. Automatic Synthesis of Microcontroller Assembly Code Through Linear Genetic Programming

Without Abstract
Douglas Mota Dias, Marco Aurélio C. Pacheco, José F. M. Amaral


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