Fukunaga Lab
The University of Tokyo
Department of General Systems Studies
Graduate School of Arts and Sciences

Japanese page
  • Research Areas
  • Lab Members
  • Publications
  • Software
  • Joining the lab

Contact:

We are in the Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo. We are also affiliated with the Graduate Program on Environmental Sciences (GPES)

Research Areas

Artificial Intelligence, Heuristic Search, Planning, Constraint Programming, Parallel Programming, Combinatorial Optimization, Machine Learning, Metaheuristics, Evolutionary Computation, and Autonomous Robotics.

Current Lab Members

  • Faculty: Alex Fukunaga
  • Ph.D. Students: Takumi Shimoda, Ryotaro Inoue
  • Masters Students: Noritomo Mushika, Xiyui Guo
  • Undergraduate Students: Sheldon Zhang, Ignatius Tan

Former Lab Members

Listed in reverse chronological order of completion. Members who completed multiple degrees in the lab are listed under their final degree.
  • Ph.D: Tomofumi Kitamura, Masataro Asai, Ryoji Tanabe
  • Masters: Guangting Liu, Yuta Takata, Jing Du, Yu Liu, Ryo Kuroiwa, Nobutaka Ito, Shunji Lin, Shuwa Miura, Takefumi Yamamura, Satoru Horie, Yuu Jinnai, Kazuto Nobuhara, Yasutaka Tanaka, Shoma Endo, Yuki Nakano, Masahiro Inohana
  • Undergraduate: Hideaki Takahashi (Informatics), Taito Ohsumi (Informatics), Dekai Wah (Environmental Science), Teppei Sassa (Informatics), Kouhei Kimijima (Informatics), Claire Cheong (Environmental Science), Shaun Zen (Environmental Science)
  • JSPS Summer Program /NSF EAPSI Fellows: Richard Freedman (University of Massachussetts, Amherst), Alex Dow (University of California, Los Angeles)

Selected Publications (for the full list of publications, see below)

  • Takahashi H, Fukunaga A. On the Transit Obfuscation Problem. 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-24), 2024. (pdf).
  • Shimoda T, Fukunaga A. Improved Exploration of the Bench Transition System in Parallel Greedy Best First Search 16th International Symposium on Combinatorial Search (SoCS-23), 2023. (pdf).
  • Asai M, Kajino H, Fukunaga A, Muise C. Classical Planning in Deep Latent Space Journal of Artificial Intelligence Research (JAIR), 74:1599-1686, 2022. (pdf).
  • Kuroiwa R, Fukunaga A. Front-to-Front Heuristic Search for Satisficing Classical Planning. 29th International Joint Conference on Artificial Intelligence (IJCAI-2020) (pdf)
  • Kuroiwa R, Fukunaga A. Analyzing and Avoiding Pathological Search Behavior in Parallel Best-First Search. 30th Int. Conf. on Automated Planning and Scheduling (ICAPS-2020). (pdf)
  • Tanabe R, Fukunaga A. Reviewing and Benchmarking Parameter Control Methods in Differential Evolution. IEEE Transactions on Cybernetics. Vol 50(3), 2020. (pdf)
  • Asai M, Fukunaga A. Classical Planning in Deep Latent Space: Bridging the Subsymbolic -Symbolic Boundary. Proc. 32nd AAAI Conference on Artificial Intelligence (AAAI-18). (pdf). An extended version of the manuscript is at: arXiv 1705.00154.
  • Jinnai Y, Fukunaga A. On Hash-Based Work Distribution Methods for Parallel Best-First Search. Journal of Artificial Intelligence Research (JAIR), 60:491-548, 2017. (pdf).
  • Jinnai Y, Fukunaga A. Learning to Prune Dominated Action Sequences in Online Black-box Planning. Proc. 31st AAAI Conference on Artificial Intelligence (AAAI-17)(pdf)
  • Asai M, Fukunaga A. Tiebreaking Strategies for Cost-Optimal Best-First Search. Journal of Artificial Intelligence Research (JAIR), 58:67-121, 2017. (pdf) [Significantly extends and expands upon our AAAI-16 paper]
  • Imai T, Fukunaga A. On a Practical, Integer-Linear Programming Model for Delete-Free Tasks and its Use as a Heuristic for Cost-Optimal Planning. Journal of Artificial Intelligence Research (JAIR), 54:631-677, 2015.(pdf)
  • Tanabe R, Fukunaga A. Success-History Based Parameter Adaptation for Differential Evolution. Proc. of IEEE Congress on Evolutionary Computation (CEC-2013)(pdf)(Ryoji's SHADE code).
    • L-SHADE, Ryoji's improved implementation of SHADE, was the winner of the CEC2014 Competition on Real-Parameter Single Objective Optimization. Many winners and high performers in subsequent competitions are based on SHADE.

List of Publications

Below, I list the papers which present the "final versions" of the research results. Thus, workshop papers aren't listed if they've been superseded by later, archival conference or journal papers. However, workshop papers with unique materials not found in later papers are listed.]
  1. Takahashi H, Fukunaga A. On the Transit Obfuscation Problem. 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-24), 2024.(pdf).
  2. Shimoda T, Fukunaga A. Separate Generation and Evaluation for Parallel Greedy Best-First Search. ICAPS Workshop on Heuristics and Search fo Domain-Independent Planning ( HSDIP-24), 2024.(pdf).
  3. Shimoda T, Fukunaga A. Improved Exploration of the Bench Transition System in Parallel Greedy Best First Search. 16th International Symposium on Combinatorial Search (SoCS-23), 2023. (pdf).
  4. Takata Y, Fukunaga A. Plausibility-Based Heuristics for Laten Space Classical Planning. ICAPS Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS-23), 2023. (pdf).
  5. Asai M, Kajino H, Fukunaga A, Muise C. Classical Planning in Deep Latent Space Journal of Artificial Intelligence Research (JAIR), 74:1599-1686, 2022. (pdf).
  6. Kitamura T, Fukunaga A. Differential Evolution with an Unbounded Population. IEEE Congress on Evolutionary Computation (CEC2022) (pdf)
  7. Kitamura T, Fukunaga A. Duplicate Individuals in Differential Evolution. IEEE Congress on Evolutionary Computation (CEC2022) (pdf)
  8. Kuroiwa R, Fukunaga A. Front-to-Front Heuristic Search for Satisficing Classical Planning. 29th International Joint Conference on Artificial Intelligence (IJCAI-2020) (pdf)
  9. Kuroiwa R, Fukunaga A. Analyzing and Avoiding Pathological Search Behavior in Parallel Best-First Search. 30th Int. Conf. on Automated Planning and Scheduling (ICAPS-2020). (pdf)
  10. Kitamura T, Fukunaga A. Revisiting Success-Histories for Adaptive Differential Evolution. IEEE Congress on Evolutionary Computation (CEC2020)
  11. Tanabe R, Fukunaga A. Reviewing and Benchmarking Parameter Control Methods in Differential Evolution. IEEE Transactions on Cybernetics. Vol 50(3), 2020. (pdf)
  12. Liu Y, Kuroiwa R, Fukunaga A. Learning Search-Space Specific Heuristics Using Neural Networks. Proc. ICAPS Workshop on Heuristics and Search for Domain-Independent Planning (HSDIP-2020)
  13. Nishi T, Otaki K, Okoso A, Fukunaga A. Cooperative Routing Problem between Customers and Vehicles for On-demand Mobile Facility Services. Proc. IEEE 23rd International Conference on Intelligent Transportation (ITSC-2020) (pdf)
  14. Kuroiwa R, Fukunaga A. On the Pathological Search Behavior of Distributed Greedy Best-First Search. 29th Int. Conf. on Automated Planning and Scheduling (ICAPS-2019). ( pdf)
  15. Kuroiwa R, Fukunaga A. A Case Study on the Importance of Low-Level Algorithmic Details in Domain-Independent Heuristics. 12th Annual Symposium on Combinatorial Search. (SoCS-2019) (pdf)
  16. Kuroiwa R, Fukunaga A. Batch Random Walk for GPU-Based Classical Planning. 28th Int. Conf. on Automated Planning and Scheduling (ICAPS-2018). (pdf)
  17. Asai M, Fukunaga A. Classical Planning in Deep Latent Space: Bridging the Subsymbolic -Symbolic Boundary. Proc. 32nd AAAI Conference on Artificial Intelligence (AAAI-18). (pdf). An extended version of the manuscript is at: arXiv 1705.00154.
  18. Lin S, Fukunaga A. Revisiting Immediate Duplicate Detection in External Memory Search. Proc. 32nd AAAI Conference on Artificial Intelligence (AAAI-18) (pdf)
  19. Fukunaga A, Botea A, Jinnai Y, Kishimoto A. Parallel A* for State-Space Search". In: Hamadi Y., Sais L. (eds) Handbook of Parallel Constraint Reasoning. Springer, Cham. 2018. This book chapter is based on the freely available arxiv paper:
    • Fukunaga A, Botea A, Jinnai Y, Kishimoto A. A Survey of Parallel A*. arXiv:1708.05296
  20. Jinnai Y, Fukunaga A. On Hash-Based Work Distribution Methods for Parallel Best-First Search. Journal of Artificial Intelligence Research (JAIR), 60:491-548, 2017. (pdf). [In addition to unifying and extending the work in our AAAI-16 and ICAPS-16 papers, this introduces a new graph partitioning based work distribution model]
  21. Tanabe R, Fukunaga A. TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods. Proc. ACM Genetic and Evolutionary Computation Conference (GECCO-2017)(pdf). Nominated for Best Paper
  22. Asai M, Fukunaga A. Exploration Among and Within Plateaus in Greedy Best-First Search. Proc. 27th Int. Conf. on Automated Planning and Scheduling (ICAPS-2017)(pdf)
  23. Miura S, Fukunaga A. Automated Extraction of Axioms for Planning. Proc. 27th Int. Conf. on Automated Planning and Scheduling (ICAPS-2017)(pdf). Honorable Mention, Best Student Paper Award
  24. Horie S, Fukunaga A. Block-Parallel IDA* for GPUs. Proc. 10th Annual Symposium on Combinatorial Search SoCS-2017((pdf of SoCS version))((extended version at arXiv)
  25. Miura S, Fukunaga A. Axioms in Model-Based Planners. Proc. ICAPS Workshop on Planning, Search, and Optimization (PlanSOpt-17).
  26. Asai M, Fukunaga A. Tiebreaking Strategies for Cost-Optimal Best-First Search. Journal of Artificial Intelligence Research (JAIR), 58:67-121, 2017. (pdf) [Significantly extends our AAAI-16 paper]
  27. Jinnai Y, Fukunaga A. Learning to Prune Dominated Action Sequences in Online Black-box Planning. Proc. 31st AAAI Conference on Artificial Intelligence (AAAI-17)(pdf)
  28. Tanabe R, Fukunaga A. How Far Are We From an Optimal, Adaptive DE?, Proc. Parallel Problem Solving from Nature (PPSN-2016), Edinburgh, September, 2016, (pdf )
  29. Jinnai Y, Fukunaga A. Automated Creation of an Efficient Work Distribution Method for Parallel Best-First Search. Proc. 26th International Conference on Automated Planning and Scheduling (ICAPS-2016) (accepted).
  30. Endo S, Asai M, Fukunaga A. Plan Optimization Based on Windows and Optimization Algorithms Applied in Sequence. Proc. ICAPS Workshop on Heuristics and Search for Domain-Independent Planning (HSDIP-2017)
  31. Asai M, Fukunaga A. Tiebreaking Strategies for A* Search: How to Explore the Final Frontier. Proc. 30th AAAI Conference on Artificial Intelligence (AAAI-16), pp.673-679(pdf)
  32. Jinnai Y, Fukunaga A. Abstract Zobrist Hash: An Efficient Work Distribution Method for Parallel Best-First Search. Proc. 30th AAAI Conference on Artificial Intelligence (AAAI-16), pp.717-723 pdf)
  33. Imai T, Fukunaga A. On a Practical, Integer-Linear Programming Model for Delete-Free Tasks and its Use as a Heuristic for Cost-Optimal Planning. Journal of Artificial Intelligence Research (JAIR), Volume 54, pages 631-677, 2015.(pdf)
    • [Earlier, conference paper] Imai T, Fukunaga A. A Practical, Integer-Linear Programming Model for the Delete-Relaxation in Cost-Optimal Planning. Proc. 21st European Conference on Artificial Intelligence (ECAI-2014). (pdf)
  34. Asai M, Fukunaga A. Solving Large-Scale Problems by Decomposition and Macro Generation. Proc. 25th International Confernce on Automated Planning and Scheduling (ICAPS-2015) (pdf)
  35. Tanabe R, Fukunaga A. Tuning Differential Evolution for Cheap, Medium, and Expensive Computational Budgets, Proc. IEEE Congress on Evolutionary Computation (CEC-2015), Sendai, May, 2015, pp. 2018-2025
  36. Aranha C, Tanabe R, Chassagne R, Fukunaga F. Optimization of Oil Reservoir Models Using Tuned Evolutionary Algorithms and Adaptive Differential Evolution, Proc. IEEE Congress on Evolutionary Computation (CEC-2015), Sendai, May, 2015, pp. 877-884
  37. Freedman R, Fukunaga A. Integration of Planning with Plan Recognition Using Classical Planners (Extended Abstract). Artificial Intelligence and Human-Robot Interaction: Papers from the AAAI Fall Symposium, p. 48-50, 2015 (pdf)
  38. Asai M, Fukunaga A. Fully Automated Cyclic Planning for Large-Scale Manufacturing Domains. Proc. 24th International Confernce on Automated Planning and Scheduling (ICAPS-2014). (pdf)
  39. Tanabe R, Fukunaga A. Reevaluating Exponential Crossover in Differential Evolution, Proc. Parallel Problem Solving from Nature (PPSN-2014). (pdf)
  40. Tanabe R, Fukunaga A. On the Pathological Behavior of Adaptive Differential Evolution on Hybrid Objective Functions. Proc. ACM Genetic and Evolutionary Computation Conference (GECCO-2014). (pdf)
  41. Tanabe R, Fukunaga A. Improving The Search Performance of SHADE Using Linear Population Size Reduction, Proc. IEEE Congress on Evolutionary Computation (CEC-2014). (pdf)
  42. Fukunaga A. An Improved Search Algorithm for Minimal Perturbation. Proc. 19th International Conference on Principles and Practice of Constraint Programming (CP-2013)(pdf)
  43. Tanabe R, Fukunaga A. Evaluation of a Randomized Parameter Setting Strategy for Island-Model Evolutionary Algorithms. Proc. of IEEE Congress on Evolutionary Computation (CEC-2013)(pdf)
  44. Tanabe R, Fukunaga A. Success-History Based Parameter Adaptation for Differential Evolution. Proc. of IEEE Congress on Evolutionary Computation (CEC-2013)(pdf)
  45. Ochi K, Fukunaga A, Kondo C, Maeda M, Hasegawa F, Kawano Y. A Steady-State Model for Automated Sequence Generation in a Robotic Assembly System. Proc. ICAPS Scheduling and Planning Applications Workshop (SPARK-2013), pp.27-34.
  46. Kishimoto A, Fukunaga A, Botea A. Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy. Artificial Intelligence, 195:222-248, 2013. (Elsevier), (arXiv version) (If you're interested in Hash-Distributed A*, please read this journal version, which significantly expands upon the ICAPS2009 paper. HDA* was scaled up to 2400 cores (vs 128 cores in the ICAPS paper), and there are many new experiments analyzing the behavior of HDA* in-depth, as well as a comparisons to TDS).
    • [Earlier, conference paper] Kishimoto A, Fukunaga A, Botea A. Scalable, parallel best-first search for optimal sequential planning. Proc. 19th Int. Conference on Automated Planning and Scheduling (ICAPS-2009) Best Paper Award (pdf)
  47. Fukunaga A, Kishimoto A, Botea A. Iterative Resource Allocation for Memory Intensive Parallel Search Algorithms on Clouds, Grids, and Shared Clusters. Procedings of AAAI-2012. (pdf)
  48. Fukunaga A, Hiruma H, Komiya K, Iba H. Evolving Controllers for High-Level Applications on a Service Robot: A Case Study of Visitor Flow Management in an Exhibition Space. Genetic Programming and Evolvable Machines, 13(2):239-263, 2012. (pdf Draft) (SpringerLink)
    • [Earlier, conference paper - the GPEM journal version includes many more simulation experiments] Hiruma H, Fukunaga A, Komiya K, Iba H. Evolving a Goal Selection Strategy for a Robot Tour Guide. Proc. IEEE Congress on Evolutionary Computation (CEC-2011), New Orleans, pp.137-144.
  49. Fukunaga A. A Branch-and-Bound Algorithm for Hard, Multiple Knapsack Problems. Annals of Operations Research , 184:97-119, 2011. (pdf Draft) (SpringerLink)
    • [Earlier, conference version] Fukunaga A. Integrating Symmetry, Dominance, and Bound-and-bound in a Multiple Knapsack Solver. Proceedings of the Fifth International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR-08), Paris, France, 2008. (pdf)
  50. Gong Y, Fukunaga A. A Distributed, Island-Model Genetic Algorithm Using Heterogeneous Parameter Settings. Proc. IEEE Congress on Evolutionary Computation (CEC-2011), New Orleans, pp.137-144. (pdf)
  51. Akagi Y, Kishimoto A, Fukunaga A. On Transposition Tables for Single-Agent Search and Planning: Summary of Results. Proc. 3rd Annual Symposium on Combinatoril Search (SoCS), pp.2-9, 2010. (pdf)
  52. Fukunaga A. Search spaces for min-perturbation repair. Proc. 15th Int. Conf. on Principles and Practice of Constraint Programming (CP-2009), pp.383-390, 2009. (pdf)
  53. Fukunaga A. A Massively Parallel GP for Discovering Satisfiability Solvers. Proc. IEEE Congress on Evolutionary Computation (CEC-2009), Trondheim, Norway, May, 2009.
  54. Fukunaga A, Tazoe S. Combining Multiple Representations in a Genetic Algorithm for the Multiple Knapsack Problem Proc. IEEE Congress on Evolutionary Computation (CEC-2009), Trondheim, Norway, May, 2009.
  55. Gong Y, Fukunaga A. Fault Tolerance in Distributed Genetic Algorithms with Tree Topologies IEEE Congress on Evolutionary Computation (CEC-2009), Trondheim, Norway, May, 2009.
  56. Fukunaga A. Automated discovery of local search heuristics for satisfiability testing. Evolutionary Computation (MIT Press), Vol 16, Number 1, pp.31-61, 2008. (pdf) (significantly expands upon the GECCO04 and AAAI02 papers below)
    • Fukunaga A. Evolving Local Search Heuristics for SAT. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), Seattle, WA, 2004. [Human-Competitive Results Award, Silver Prize] (pdf) [Describes the 2nd implementation of CLASS (in Common Lisp), which results in a much faster solver compared to the AAAI02 implementation, and outperformed fast C implementation of Walksat and Novelty+]
    • Fukunaga A. Automated Discovery of Composite SAT Variable-Selection Heuristics. Proceedings of the National Conf. on Artificial Intelligence), 2002 (AAAI-02), Edmonton, Alberta, Canada, pp.641-648. (pdf) [Describes the first implementation of CLASS (in C++ and tcl). While the search was efficient (with respect to the # of flips to solve problems), the runtime was not impressive due to the ugly implementation of the embedded heuristic]
  57. Fukunaga A, Korf RE. Bin-completion algorithms for multicontainer packing, knapsack, and covering problems. Journal of Artificial Intelligence Research. vol 28, pp.393-429, 2007. (pdf)
    • [Earlier, conference version] Fukunaga A, Korf RE. Bin-completion algorithms for multicontainer packing and covering problems. Proc. International Joint Conference on Artificial Intelligence, 2005. (IJCAI-05) Edinburgh, Scotland, pp.117-124. (pdf)
  58. Castano A, Fukunaga A, Biesiadecki J, Neakrase L, Whelley P, Greeley R, Lemmon M, Castano R, Chien S. Automatic detection of dust devils and clouds on Mars. Machine Vision and Applications. vol 19(5-6), pp.467-482, 2008. (pdf)
    • [Earlier, conference version] Castano A, Fukunaga A, Biesiadecki J, Neakrase L, Whelley P, Greeley R, Lemmon M, Castano R, Chien S. Autonomous detection of dust devils and clouds on Mars. Proceedings of the International Conference on Image Processing (ICIP-06), Atlanta, GA 2006, pp.2765-2768.
    • Chien S, Castano R, Bornstein B, Fukunaga A, Castano A, Biesiadecki J, Greeley R, Whelley P, Lemmon M. Results from Automated Cloud and Dust Devil Detection Onboard the MER rovers. Proceedings of the Ninth International Symposium on Artificial Intelligence, Robotics, and Automation in Space (i-SAIRAS-08), Los Angeles, CA 2008. [This i-SAIRAS08 paper reports some results obtained using the software after it was actually deployed on Mars (after the journal paper was in press)]
  59. Fukunaga A. A new grouping genetic algorithm for the multiple knapsack problem. Proc IEEE Congress on Evolutionary Computation (CEC2008), Hong Kong, China.pp.2225-2232, June 1-6, 2008. multiple knapsack problem instances used in this paper
  60. Castano R, Estlin T, Gaines D, Castano A, Chouinard C, Bornstein B, Anderson R, Chien S, Fukunaga A, Judd M. Opportunistic Rover Science: Finding and Reacting to Rocks, Clouds, and Dust Devils. Proceedings of the IEEE Aerospace Conference, March 2006. (pdf)
  61. Fukunaga A. Efficient Implementation of SAT Local Search. Proceedings of the Seventh International Conf. on Theory and Applications of Satisfiability Testing (SAT-2004), Vancouver, British Columbia, 2004. (pdf)
  62. Fukunaga A, Rabideau G, Chien S Robust local search for spacecraft operations using adaptive noise. Proceedings of the 4th International Workshop on Planning and Scheduling for Space (IWPSS-04), Darmstadt, Germany, 2004 (pdf)
  63. Fukunaga A. Complete restart strategies using a compact representation of the explored search space. IJCAI-03 Workshop on Stochastic Search Algorithms, Acapulco, Mexico. Note: several typographical errors in the original workshop paper have been corrected (In the example in page 3, Section 2, some AND's and OR's were unintentionally reversed). (pdf)
  64. Fukunaga A, Hamilton E, Fama J, Andre D, Matan O, Nourbakhsh. Staff Scheduling for Inbound Call Centers and Customer Contact Centers. AI Magazine 23(4): Winter 2002, pp.30-40. Special issue on selected papers from Innovative Applications of AI 2002. (pdf)
    • [Earlier, conference version] Fukunaga A, Hamilton E, Fama J, Andre D, Matan O, Nourbakhsh. Staff Scheduling for Inbound Call Centers and Customer Contact Centers. Proc. Innovative Applications of Artificial Intelligence, 2002 (IAAI-02), Edmonton, Alberta, Canada.
  65. Fukunaga A. Genetic Algorithm Portfolios. Proceedings of the IEEE Congress on Evolutionary Computation (CEC-2000), San Diego, CA, 2000. (pdf)
  66. Rabideau G, Knight R, Chien S, Fukunaga A, Govindjee A. Iterative repair planning for spacecraft operations in the ASPEN system. Proceedings of the International Syposium on Artificial Intelligence, Robotics, and Automation in Space (i-SAIRAS99), Noordwijk, The Netherlands, June, 1999. (pdf)
  67. Smith B, Sherwood R, Govindjee A, Yan D, Rabideau G, Chien S, Fukunaga A. Representing Spacecraft Mission Planning Knowledge in ASPEN. AIPS-98 Workshop on Knowledge Engineering and Acquisition for Planning, June 1998. (AAAI Technical Report WS-98-03), pp. 58-72. (pdf)
  68. Stoica A, Fukunaga A, Hayworth K. Evolvable Hardware for Space Applications. Proceedings of the International Conference on Evolvable Systems (ICES-98), Lausanne, Switzerland, August, 1998. (pdf)
  69. Sherwood R, Govindjee A, Yan D, Rabideau G, Chien S, Fukunaga A. Using ASPEN to Automate EO-1 Activity Planning. Proceedings of the IEEE Aerospace Conference, Snowmass, CO, March, 1998. (pdf)
  70. Fukunaga A. Restart Scheduling for Genetic Algorithms. Proceedings of the 5th International Conference on Parallel Problem Solving from Nature (PPSN-V), Leiden, Netherlands, September, 1998. (pdf)
  71. Fukunaga A, Stechert A, Mutz D. A genome compiler for high-performance genetic programming. Proceedings of the 3rd Annual Genetic Programming Conference, 1998 (GP-98). (pdf)
  72. Fukunaga A, Stechert A. Evolving nonlinear predictive models for lossless image compression with genetic programming. Proceedings of the 3rd Annual Genetic Programming Conference, 1998 (GP-98). (pdf)
  73. Fukunaga A. Rabideau G, Chien S, Yan D. ASPEN: An Application Framework for Automated Planning and Scheduling of Spacecraft Control and Operations. Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS97), Tokyo, Japan, 1997, pp. 181-187 (pdf)
  74. Fukunaga A. Variable-Selection Heuristics in Local Search for Satisfiability Testing. Proceedings of the National Conference on Artificial Intelligence (AAAI-97), Providence, RI, 1997, pp.275-280. (pdf)
  75. Fukunaga A. Application of an Incremental Evolution Technique to Spacecraft Design Optimization. Proc. IEEE International Conference on Evolutionary Computation (ICEC'97), Indianapolis, IN, 1997, pp. 431-435
  76. Fukunaga A, Chien S, Mutz D, Sherwood R, Stechert A. Automating the Process of Optimization in Spacecraft Design. Proc. IEEE Aerospace Conference, Snowmass CO, 1997, vol. 4 pp. 411-428 (pdf) [Describes OASIS, a spacecraft design optimization system which includes the black-box optimizer described in the IEA/AIE paper below, as well as a meta-level optimizer which tries to match an appropriate configuration of the black-box optimizer to each problem instance.]
    • Fukunaga A, Stechert A. An Evolutionary Optimization System for Spacecraft Design. Proceedings of the Tenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-97)}, Atlanta, GA, 1997, pp.1-6. [Describes DEVO (Design EVOlver), which is the black-box optimization component of the OASIS system]
  77. Cao Y, Fukunaga A, Kahng AB. Cooperative Mobile Robotics: Antecedents and Directions. Autonomous Robots vol 4, 1997. [Reprinted as a chapter in the book: Robot Colonies, Arkin RC and Bekey GA, eds., Kluwer Academic Press, ISBN 0-7923-9904-8, 1997] (pdf)
    • [Earlier, conference version] Cao Y, Fukunaga A, Kahng AB, Meng F. Cooperative Mobile Robotics: Antecedents and Directions. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 1995 (IROS-95), pp.226-234.
  78. Fukunaga A, Huang DJ-H, Kahng AB. Large-Step Markov Chain Variants for Netlist Partitioning. Proceedings of the IEEE Int. Symposium on Circuits and Systems, 1996 (ISCAS-96)., pp.496-9 (vol 4). (pdf)
  79. Auslander J, Fukunaga A, Partovi H, Christensen J, Hsu L, Reiss P, Shuman A, Marks J, Ngo JT. Further Experience with Controller-Based Automatic Motion Synthesis for Articulated Figures. ACM Transactions on Graphics, vol 14, no. 4, pp. 311-336, 1995. (pdf)
      Additional details on 2D motion synthesis can be found in the following technical reports. TR-05-94 was submitted to SIGGRAPH94 but it was rejected (my first rejected paper!), so it was combined with the 3D motion synthesis work of Auslander and Partovi and submitted to the ACM Transactions on Graphics.
    • Fukunaga A, Christensen J, Ngo JT, Marks J. A Comparative Study of Search and Optimization Algorithms for the Automatic Control of Physically Realistic 2-D Animated Figures. Technical Report TR-23-94, Center for Research in Computing Technology, Harvard University, August, 1994.
    • Fukunaga A, Hsu L, Reiss P, Shuman A, Christensen J, Marks J, Ngo JT. Motion Synthesis Techniques for 2-D Articulated Figures. Technical Report TR-05-94, Center for Research in Computing Technology, Harvard University, March, 1994.
  80. Fukunaga A, Kahng AB. Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evaluation Function. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC-95), November, 1995, pp. 182-187. (pdf)
  81. Fukunaga A, Ngo JT, Marks J. Solving the Spacetime Constraints Problem Using Evolutionary Programming Algorithms. Proceedings of the 3rd Annual Conference on Evolutionary Programming (EP-94), San Diego, CA, World Scientific, 1994. (pdf)
  82. Fukunaga A, Kimura TD, Pree W. Object-Oriented Development of a Data Flow Visual Language System. Proceedings of the IEEE/CS Symposium on Visual Languages (VL-93), Bergen, Norway, IEEE Press, 1993, pp. 134-141. (pdf)
  83. Fukunaga A, Pree W, Kimura TD. Functions as Data Objects in a Data Flow Based Visual Language. Proceedings of the ACM Computer Science Conference, Indianapolis, IN, ACM Press, 1993.

Software

  • CL-MPI - A portable, Common Lisp binding for the Message Pasing Interface (MPI). Enables message-passing based parallel Common Lisp programming on either a cluster or a single multi-core machine.

Information for students interested in joining the lab

  • Information for Japanese applicants
  • Information for international applicants

All content copyright © 2015 Alex Fukunaga, all rights reserved.


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