Yuri R. Tsoy, Vladimir G. Spitsyn, Department of Computer Engineering icon

Yuri R. Tsoy, Vladimir G. Spitsyn, Department of Computer Engineering



НазваниеYuri R. Tsoy, Vladimir G. Spitsyn, Department of Computer Engineering
Дата конвертации25.10.2012
Размер445 b.
ТипДокументы


USING DESIGN PATTERNS FOR DESIGN OF SOFTWARE ENVIRONMENT FOR RESEARCHES IN GENETIC ALGORITHMS

  • Yuri R. Tsoy, Vladimir G. Spitsyn,

  • Department of Computer Engineering

  • Tomsk Polytechnic University

  • neuroevolution@mail.ru


Report structure

  • 1. Introduction

  • 2. Software environment description

  • 3. Genetic algorithm and design patterns

  • 4. Flexibility and extensibility

  • 5. Conclusion



Design patterns and software design

  • Design pattern - the way of cooperation of objects and classes that are adapted for common design task solution in certain context. (by Erich Gamma, Richard Helm, Ralph Johnson and John Vlissides)

  • Main advantages available with use of design patterns:

  • reusability

  • flexibility

  • extensibility



Researches in genetic algorithms

  • Problems:

  • No specialized software environment for algorithm’s parameters settings and control, experimental data processing and visualization.

  • No methodology of flexible software implementation of genetic algorithms.





Common program architecture



Interface unit

  • Isolates all blocks from each other. All communications are made via Interface unit using unified data format.

  • This helps to combine different implementations of different blocks.



Genetic algorithm block

  • Provides implementation of some model of genetic algorithm.

  • Block is separated from other system’s blocks for purposes of use of different genetic algorithm models for one and the same task solution.



Task environment block

  • Provides implementation of some task environment. Can be used for both modeling and evaluation purposes.

  • Block is separated from other system’s blocks for purposes of use of different tasks with one and the same genetic algorithm model.



Data analysis block

  • Provides instruments and methods for analysis of data from both Genetic algorithm and Task environment blocks.

  • Block is separated from other system’s blocks for purposes of use of different data analysis approaches and implementations. In fact can consist of several sub-blocks.



Visualization block

  • Provides visualization of data processed by Data analysis block or passed from Genetic algorithm and Task environment block.

  • Block is separated from other system’s blocks for purposes of use of different data visualization algorithms and methods.



General block structure



Report structure

  • 1. Introduction

  • 2. Software environment description

  • 3. Genetic algorithm and design patterns

  • 4. Flexibility and extensibility

  • 5. Conclusion



Taking a deeper look at Genetic algorithm block

  • Following parameters of genetic algorithm can vary depending on given GA model:

  • genetic representation;

  • population initialization;

  • population evaluation;

  • selection strategy;

  • reproduction strategy (parental pair selection);

  • genetic operators;

  • next generation formation.



Taking a deeper look at Genetic algorithm block (2)

  • Parameters objects created by the Factory patterns:

  • genetic representation;

  • population initialization;

  • genetic operators;

  • Task (algorithm type) dependent parameters objects

  • population evaluation;

  • Universal (task independent) parameters objects:

  • selection strategy;

  • reproduction strategy (parental pair selection);

  • next generation formation.



Report structure

  • 1. Introduction

  • 2. Software environment description

  • 3. Genetic algorithm and design patterns

  • 4. Flexibility and extensibility

  • 5. Conclusion



Flexibility and extensibility (1)

  • Introduced architecture allows to:

  • Implement blocks as components of different specialized software packages.

  • Combine different programs and make them work together using appropriate data and control adapters.

  • Organize inner structure of certain block in any way.

  • Combine blocks independently and transparently for other blocks. For example, Data analysis and Visualization blocks can be united in one larger unit without any changes in other blocks.



Flexibility and extensibility (2)

  • Introduced architecture allows to:

  • Modify blocks without any changes in other parts of the introduced system. It is possible to change operators or encoding method in Genetic algorithm block but other blocks will never “know” about it.

  • Extend blocks functionality independently. For example it is possible to add some new method of data analysis in appropriate block then make some changes in control adapter and use new ability.



Report structure

  • 1. Introduction

  • 2. Software environment description

  • 3. Genetic algorithm and design patterns

  • 4. Flexibility and extensibility

  • 5. Conclusion



Conclusion

  • The result of this project is intended for research purposes and will be distributed free. Introduced software architecture can be applied for many other research areas.

  • First release: approximately 2-nd quarter of 2005. Source code and documentation will be available too.



Thank you for your attention!





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