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IEEE International Conference on Systems, Man and Cybernetics

作者： Watchareeruetai, Ukrit Matsumoto, Tetsuya Takeuchi, Yoshinori Kudo, Hiroaki Ohnishi, Noboru Nagoya Univ Grad Sch Informat Sci Dept Media Sci Chikusa Ku Nagoya Aichi 4848603 Japan

ISBN: (纸本)9781424427932

This paper proposes a multi-objective genetic programming (MOGP) for automatic construction of feature extraction programs (FEPs). The proposed method is modified from a well known non-dominated sorting evolutionary algorithm, i.e., NSGA-II. The key differences of the method are related with redundancies in program representation. We apply redundancy regulations in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity. Experimental results exhibit that the proposed MOGP-based FEPs construction system provides obviously better performance than the original non-dominated sorting approach.

关键词： Multi-objective optimization linear genetic programming image feature extraction redundancy regulation non-dominated sorting

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A linear structured approach and a refined fitness function in genetic programming for multi-class object classification

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CONNECTION SCIENCE 2007年第4期19卷 339-359页

作者： Zhang, Mengjie Fogelberg, Christopher Graeme Ma, Yuejin Victoria Univ Wellington Sch Math Stat & Comp Sci Wellington New Zealand Agr Univ Hebei Coll Mech & Elect Engn Baoding 071001 Peoples R China

This paper describes an approach to the use of genetic programming (GP) to multi-class object recognition problems. Rather than using the standard tree structures to represent evolved classifier programs which only produce a single output value that must be further translated into a set of class labels, this approach uses a linear structure to represent evolved programs, which use multiple target registers each for a single class. The simple error rate fitness function is refined and a new fitness function is introduced to approximate the true feature space of an object recognition problem. This approach is examined and compared with the tree based GP on three data sets providing object recognition problems of increasing difficulty. The results show that this approach outperforms the standard tree based GP approach on all the tasks investigated here and that the programs evolved by this approach are easier to interpret. The investigation into the extra target registers and program length results in heuristic guidelines for initially setting system parameters.

关键词： linear genetic programming program structure program representation fitness function multi-class classification object classification object recognition

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A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia

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NEURAL NETWORKS 2008年第2-3期21卷 379-386页

作者： Floares, Alexandru George Inst Oncol Dept Artificial Intelligence Cluj Napoca 400015 Transylvania Romania SAIA Cluj Napoca 400310 Transylvania Romania

Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which call be used to reverse engineer neural networks. The RODES algorithim automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： neural networks reverse engineering algorithm linear genetic programming systems of ordinary differential equations basal ganglia discovery science approach

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Evolving evolutionary algorithms with patterns

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SOFT COMPUTING 2007年第6期11卷 503-518页

作者： Oltean, Mihai Univ Babes Bolyai Fac Math & Comp Sci Dept Comp Sci R-3400 Cluj Napoca Romania

A new model for evolving evolutionary algorithms (EAs) is proposed in this paper. The model is based on the multi expression programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme which is used for solving a particular problem. Several evolutionary algorithms for function optimization are evolved by using the considered model. The evolved evolutionary algorithms are compared with a human-designed genetic algorithm. Numerical experiments show that the evolved evolutionary algorithms can compete with standard approaches for several well-known benchmarking problems.

关键词： genetic algorithms genetic programming evolving evolutionary algorithms multi expression programming linear genetic programming

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On the impact of migration parameters on DIMEP for designing combinational circuits

On the impact of migration parameters on DIMEP for designing...

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26th International Conference of the Chilean-Society-of-Computer-Science

作者： Hadjam, Fatima Zohra Moraga, Claudio Rahmouni, Mustapha Kamel Univ Djillali Liabes Sidi Bel Abbes Sidi Bel Abbes Algeria Univ Dortmund Dortmund Germany Univ Essenia Oran Algeria

ISBN: (纸本)9780769530178

This paper proposes an Island Model-based parallel linear genetic programming methodology: Distributed Multi Expression programming (DMEP) to support the design of combinational logic circuits and investigates how the migration policy (the migration period, the number of migrants and the migration topology) affects the behavior of the evolutionary process in term of different statistics (computational effort, percentage of successful runs and average fitness) depending on the type and the size of the problems being solved. Two benchmark problems are considered: multiplier circuits and n-bit even parity circuits.

关键词： linear genetic programming distributed processing Islands model circuit design

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Geometry and sizing optimisation of discrete structure using the genetic programming method

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2006年第6期31卷 452-461页

作者： Zheng, Q. Z. Querin, O. M. Barton, D. C. Univ Leeds Sch Mech Engn Leeds LS2 9JT W Yorkshire England

This paper presents a structural optimisation method using the genetic programming (GP) technique. This method applied linear GP to derive optimum geometry and sizing of discrete structure from an arbitrary initial design space. The linear GP was used to find out the optimum nodal locations and member sizing of the structure through a linear sequence of programming instructions. The nodal locations and member cross-sectional areas of the structure were used as the design variable for these instructions, with the optimal geometry and sizing obtained by evolving a population of GP individuals satisfying the optimisation design objective. The approach was applied to the benchmark example of ten-bar planar truss for verification. Other truss examples, including 18-bar planar truss and 25-bar space truss, were also used to demonstrate the effectiveness of this method. The optimum results obtained demonstrate the practicability and generality of using the proposed method in geometry and sizing optimisation problems.

关键词： linear genetic programming discrete structure geometry and sizing optimisation

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genetic parallel programming: Design and implementation

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EVOLUTIONARY COMPUTATION 2006年第2期14卷 129-156页

作者： Cheang, Sin Man Leung, Kwong Sak Lee, Kin Hong Hong Kong Inst Vocat Educ Dept Comp Kwai Chung Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Sha Tin Hong Kong Peoples R China

This paper presents a novel genetic Parallel programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.

关键词： genetic programming linear genetic programming genetic parallel programming parallel processor architecture

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On evolving buffer overflow attacks using genetic programming 06

On evolving buffer overflow attacks using genetic programmin...

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8th Annual genetic and Evolutionary Computation Conference

作者： Kayacik, Hilmi Guenes Heywood, Malcolm Zincir-Heywood, Nur Dalhousie Univ Fac Comp Sci 6050 Univ Ave Halifax NS Canada

ISBN: (纸本)9781595931863

In this work, we employed genetic programming to evolve a "white hat" attacker;that is to say, we evolve variants of an attack with the objective of providing better detectors. Assuming a generic buffer overflow exploit, we evolve variants of the generic attack, with the objective of evading detection by signature-based methods. To do so, we pay particular attention to the formulation of an appropriate fitness function and partnering instruction set. Moreover, by making use of the intron behavior inherent in the genetic programming paradigm, we are able to explicitly obfuscate the true intent of the code. All the resulting attacks defeat the widely used 'Snort' Intrusion Detection System.

关键词： linear genetic programming mimicry attacks intrusion detection systems

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Evolving recurrent models using linear GP 05

Evolving recurrent models using linear GP

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Proceedings of the 7th annual conference on genetic and evolutionary computation

作者： Xiao Luo Malcolm I. Heywood A. Nur Zincir-Heywood Dalhousie University Halifax Canada

ISBN: (纸本)9781595930101

Turing complete genetic programming (GP) models introduce the concept of internal state, and therefore have the capacity for identifying interesting temporal properties. Surprisingly, there is little evidence of the application of such models to problems for prediction. An empirical evaluation is made of a simple recurrent linear GP model over standard prediction problems.

关键词： recurrent architectures linear genetic programming

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Evoluční návrh pohybujících se objektů

Evoluční návrh pohybujících se objektů

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作者： Fajkus, Jakub Brno University of Technology

Cílem této práce je implementovat systém pro automatizovaný evoluční návrh kontrolérů virtuálních robotů. Pro nalezení vhodného programu, který bude ř&#... 详细信息

Cílem této práce je implementovat systém pro automatizovaný evoluční návrh kontrolérů virtuálních robotů. Pro nalezení vhodného programu, který bude řídit robota tak, že se bude pohybovat po trajektorii, která je definována posloupností bodů, je použita reprezentace založená na lineárním genetickém programování ve spojení s genetickým algoritmem. Pro vyhodnocení chování robota, kterého křídí kandidátní řešení vygenerované genetickým algoritmem, je použit fyzikální simulátor MuJoCo, který uživateli dovoluje definovat tvar robota. Cílem evoluce ja natrénovat kontrolér robota tak, aby následoval definovanou trasu. Trénování kontroléru robota je založeno na optimalizaci vzdálenosti mezi robotem a body definujícími trajektorii. Optimalizace se provádí evolucí kontrolérů po daný počet generací steady-state genetického algoritmu. Je zde prezentováno několik experimentů s vyhodnocením jejich výsledků.

关键词： simulátor MuJoCo lineární genetické programování genetický algoritmus virtuální robot trajektorie MuJoCo physics engine linear genetic programming genetic Algorithm virtual robot trajectory Text

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