An adaptive framework for internet based distributed genetic algorithms

An Adaptive Framework for Internet-based Distributed Genetic Algorithms A dissertation submitted in partial fulfilment of the requirement for the degree of Doctor of Philosophy by Johan Berntsson M Sc Link¨oping University, Sweden Supervisor: Dr Maolin Tang Associate Supervisors: Dr Wayne Kelly, Associate Professor Dr Paul Roe School of Software Engineering and Data Communications Faculty of Information Technology Queensland University of Technology Brisbane, Australia Submitted for examination 31 March 2006, revised 23 July 2006 II Keywords genetic algorithms, distributed genetic algorithms, Internet computing, floorplanning, adaptation, VLSI III IV Abstract Genetic Algorithms (GAs) are search algorithms inspired by genetics and natural selection, and have been used to solve difficult problems in many disciplines, including modelling, control systems and automation GAs are generally able to find good solutions in reasonable time, however as they are applied to larger and harder problems they are very demanding in terms of computation time and memory The Internet is the most powerful parallel and distributed computation environment in the world, and the idle cycles and memories of computers on the Internet have been increasingly recognized as a huge untapped source of computation power By combining Internet computing and GAs, this dissertation provides a framework for Internet-based parallel and distributed GAs that gives scientists and engineers an easy and affordable way to solve hard real world problems Developing parallel computation applications on the Internet is quite unlike developing applications in traditional parallel computation environments, such as multiprocessor systems and clusters This is because the Internet is different in many respects, such as communication overhead, heterogeneity and volatility To develop an Internet-based GA, we need to understand the implication of these differences For this purpose, a convergence model for heterogenous and volatile networks is presented and used in experiments that study GA performance and robustness in Internet-like scenarios The main outcome of this research is an Internet-based distributed GA framework called G2DGA G2DGA is an island model distributed GA, which can provide support for big populations needed to solve many real world problems G2DGA uses a novel hybrid peer-to-peer (P2P) design with island node activity coordinated by supervisor nodes that offer a global overview of the GA search state Compared to client/server approaches, the P2P architecture improves scalability and fault tolerance by allowing direct communication between the V islands and avoiding single-point-of-failure situations One of the defining characteristics of Internet computing is the dynamics and volatility of the environment, and a parallel and distributed GA that does not adapt to its environment cannot use the available resources efficiently Two novel adaptive methods are investigated The first method is migration topology adaptation, which uses clustering on elite individuals from each island to rebuild the migration topology Experiments with the migration topology adapter show that it gives G2DGA better performance than a GA with static migration topology of a similar or larger connectivity level The second method is population size adaptation, which automatically finds the number of islands and island population sizes needed to solve a given problem efficiently Experiments on the population size adapter show that it is robust, and compares favourably with the traditional trial-and-error approach in terms of computational effort and solution quality The scalability and robustness of G2DGA has been extensively tested in network scenarios of varying volatility and heterogeneity Experiments with up to 60 computers were conducted in computer laboratories, while more complex network scenarios have been studied in an Internet simulator In the experiments, G2DGA consistently performs as well as, and usually significantly better than, static distributed GAs and the difference grows larger with increased network instability The results show that G2DGA, by continuously adjusting the migration policy and the population size, can detect and make efficient use of idle cycles donated over volatile Internet connections To demonstrate that G2DGA can be used to implement and solve real world problems, a challenging application in VLSI design was developed and used in the testing of the framework The application is a multi-layer floorplanner, which uses a novel GA representation and operators based on a slicing structure approach Its packing quality compares favourably with other multi-layer floorplanners found in the literature Internet-based distributed GA research is exciting and important since it enables GAs to be applied to problem areas where resource limitations make traditional approaches unworkable G2DGA provides a scalable and robust Internet-based distributed GA framework that can serve as a foundation for future work in the field VI Authorship The work contained in this thesis has not been previously submitted for a degree or diploma at this or any other higher education institution To the best of my knowledge and belief, the thesis contains no material previously published or written by any other person except where due reference is made Signed: Date: VII VIII For Erika and Mayumi IX X contribution is an extensive investigation of migration policy parameters in dynamic Internet networks, using the convergence model A framework for Internet-based DGAs The G2DGA framework uses a novel hybrid P2P architecture with island node activity coordinated by supervisor nodes that offer global overview and opportunities for adaptation A unique feature of the framework is that it can run either on a P2P network or in a simulator mode without requiring any recompilations or modifications Migration topology adaptation The migration topology adaptive method uses a novel clustering approach to dynamically update the migration paths between islands The adapter reduces connectivity while maintaining high solution quality The experiments in Chapter have demonstrated that the migration topology adapter gives G2DGA better speedup and more robust performance than static DGAs Population size adaptation The population size adapter automatically searches for a good combination of the number of islands, and the population size on each island, in DGAs This adapter is applicable to a wide range of GA algorithms, and makes DGAs easier to use by reducing the number of user-set parameters and aiding in construction of robust GA applications with good performance The experiments have shown that the population size adapter reliably finds the number of islands, and the population size on each island, needed to solve a given problem The population sizes found by the algorithm are comparable to those found with traditional trial-and-error approaches A novel multi-layer VLSI floorplaning application The floorplanner is an example of the kind of challenging real world problems that a typical GA practitioner works with In this NP-hard problem a novel slicing structure representation was used, and the application was implemented and tested in the G2DGA framework In the dissertation, the application was also used to supplement experimental testing of benchmark functions Scalability and robustness studies in Internet computing environments 167 The experimental study of the framework is more extensive than earlier research on similar approaches to Internet-based DGAs, e.g the DREAM project The study is a combination of computer lab experiments involving up to 60 computers, and Internet simulation runs In the experiments several network scenarios were tested, ranging from stable LAN to volatile and heterogeneous cycle-stealing environments The experimental results demonstrate that G2DGA’s supervisor/island design allows the implementation of adaptive methods in a P2P network environment, and show that adaptation is an efficient way of responding to the dynamic and volatile nature of Internet computing 10.3 Extensions While there is still much work needed to create Internet-based DGAs that are adaptive and efficient over a wide range of network conditions while still being simple enough to use without requiring extensive experimentation or expert knowledge, this research provides a scalable and robust P2P DGA framework which I believe can serve as a foundation for future work in the field However, as often happens in research, the process of answering one set of questions leads to new ideas that still remain to be answered This section briefly discusses some of these ideas that may form the basis for future research efforts either by extending work that has been presented in this dissertation, or by branching out to possible new lines of inquiry One goal of G2DGA is to make it easy for non-experts to develop and deploy GA applications on a cycle-stealing network Currently, the dotGALib support library, the G2DGA architecture, and the adaptive methods support and reduce the complexity of G2DGA development, but still require some knowledge on the design and implementation of the framework to use A possible future extension of G2DGA is to add a setup tool which provides a user interface which would determine the needs of the user by collecting basic information on the problem The tool would hide the implementation details for the non-expert user by automatically generating code and compiling the application This research has used the island model since it allows the population to scale However, 168 for many problems much of the computation overhead is attributed to evaluation of fitness functions Since these evaluations are independent of one another, they could be distributed onto other nodes for calculation An obvious extension to the current framework is therefore to use evaluation nodes in addition to the island and supervisor nodes An interesting observation made in Section 7.3.3 is that the population size adapter uses smaller population sizes than those required for reliable performance in manual experiments This may be a consequence of the nature of the population adapter, where three DGAs are run in parallel and restarted if they are stuck or found to perform badly Due to time limitations, this phenomena was never investigated in detail but it would be an interesting topic for future research to try to find an theoretical estimate on the expected population size needed, and the total effort used, compared to a non-adaptive DGA using the same problem The use of the hybrid P2P architecture allowed the collection and analysis of the global search state in the supervisor nodes In the current research this information was used to adapt the migration topology, which was identified as being one of the most important of the parameters that influence DGA performance There are also opportunities to expand the research in adaptive parameter adjustment further, using the search state or other island statistics as input Promising approaches for such research would be to adaptively adjust the island mutation or migration rates Another interesting extension to the current research would be to investigate the influence of different migration topology adaptation strategies during different stages of the search 169 170 Appendix A G2DGA Implementation Notes A.1 Software Used The following software has been used in the G2DGA development: • Microsoft Visual Studio NET 2003 with C#; • Microsoft NET framework version 1.1 ; • “DockingSuite” from “Divelements” software (http://www.divil.co.uk/net/) was used to implement docking windows in the GUI for the Analyser and Simulator packages The software is available at no charge to use in any products royalty-free, as long as copyright is acknowledged; • “FolderTreeView” by Furty (furty74@yahoo.com) was used to implement the directory view in the Analyser package The software is free for any use, so long as copyright is acknowledged; • Gnuplot version 4.0 (http://www.gnuplot.info) is used by the Analyser and Simulator to create graphs Gnuplot is released under a license that allows royalty-free usage in other products 171 A.2 File Structure The project file structure is as follows: G2DGA contains the subprojects needed to implement G2DGA bin contains executables and libraries for G2P2P and all G2DGA subprojects doc contains documentation Analyser code and make files for the Analyser software Simulator code and make files for the Simulator software G2DGA code and make files for the framework software G2DGAWizard code and make files for the set-up tool software G2DGAConsole code for the graphical front-end for the simulator or G2P2P client Simulator code and make files for the Internet emulator and G2DGA simulator dotGALib code and make files for the dotGALib library The G2DGA folder contains a Microsoft Visual Studio solution file called “g2dga.sln” By double-clicking this file and selecting Build/Build Solution in Visual Studio, all projects are compiled The executables (G2DGAConsole and Analyser) can be started by double-clicking them in the G2DGA/bin directory A.3 Documentation Doxygen (http://www.doxygen.org) was used to generate on-line and off-line documentation of the G2DGA software, by extracting data structures and comments directly from the source files The on-line documentation, in the form of cross-referenced html files that can be viewed in an Internet browser, is located in the G2DGA/doc/html directory Doxygen also creates an off-line reference manual, which can be found in the G2DGA/doc/latex directory To view and print the book, the Latex source has to be compiled using a Latex compiler and the included Makefile 172 Bibliography [1] D Abramson and J Abela A parallel genetic algorithm for solving the school timetabling problem In G Gupta and C Keen, editors, 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