research in computing: from curiosity to new theory and applications prabhas chongstitvatana faculty...
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Research in Computing:
from curiosity to new theory and applications
Prabhas Chongstitvatana
Faculty of Engineering
Chulalongkorn University
Outline
• Research career
• Research dimensions
• Research examples
• Research in computing
• Research for Thais
Research career
• Beginning: curiosity
• Midterm: experiment
• Maturity: new theory and applications
Research dimensions
• near-term long-term
• internal external
• narrow broad
Research Examples
• Learning finite state machine
• Genetic algorithm in hardware
• Building Blocks
• Scheduling in manufacturing
• Search for Lead-free Solder Alloys
1998 Synthesis of Synchronous Sequential Logic Circuits from Partial Input/Output Sequences
.
Two-Horn Chameleon (Bradypodion fischeri ssp.) in the Usambara mountains, Tanzania
2001 A Hardware Implementation of the Compact Genetic Algorithm
• Fabricate on FPGA, runs about 1,000 times faster than the software executing on a workstation.
Pseudocode of Compact GA
Hardware organization (population size = 256, chromosome length = 32)
2004 Building block identification by simulateneity matrix
• Building Blocks concept
• Identify Building Blocks
• Improve performance of GA
x = 11100 f(x) = 28x = 11011 f(x) = 27x = 10111 f(x) = 23x = 10100 f(x) = 20---------------------------x = 01011 f(x) = 11x = 01010 f(x) = 10x = 00111 f(x) = 7x = 00000 f(x) = 0
Induction 1 * * * *(Building Block)
x = 11111 f(x) = 31x = 11110 f(x) = 30x = 11101 f(x) = 29x = 10110 f(x) = 22---------------------------x = 10101 f(x) = 21x = 10100 f(x) = 20x = 10010 f(x) = 18x = 01101 f(x) = 13
1 * * * *(Building Block)
Reproduction
x = 11111 f(x) = 31x = 11110 f(x) = 30x = 11101 f(x) = 29x = 10110 f(x) = 22---------------------------x = 10101 f(x) = 21x = 10100 f(x) = 20x = 10010 f(x) = 18x = 01101 f(x) = 13
Induction 1 * 1 * *
(Building Blocks)1 1 * * *
{{0,1,2},{3,4,5},{6,7,8},{9,10,11},{12,13,14}}
2009 Combinatorial Optimization with Coincidence (COIN)
• Use both good and not-good solutions.
• A Generator represents a probabilistic model of the required solution.
• Reward and punishment schemes are incorporated in updating the generator.
Pseudo code for COIN
1. Initialize the generator.2. Generate the population using the generator.3. Evaluate the population.4. Select the candidates.
Adaptive selection: select the above and below the average ±2σ
5. For each joint probability h(xi|xj), update the generator according to the reward and punishment
6. Repeat Step 2. Until the terminate condition is met.
• Complete line assignment for straight assembly line.
• Complete line assignment for U-shaped assembly line
TABLE IV RESULT OF THE EXPERIMENT IN HWANG AND KATAYAMA’S PROBLEMS
Problems and Algorithms
Thomopolous (19 task)
Kim (61 task)
Arcus (111 task)
Benchmarking
NSGA-II COIN NSGA-II COIN NSGA-II COIN
Convergence 0.295 0 0.847 0 0.189 0
Spread 0.566 0.523 0.742 0.774 0.485 0.710
Ratio of solution 0 1 0 1 0 1
Time (min) 124 3 347 15 735 40
POPULATION SIZE = 100, GENERATION = 200 NSGA-II: CROSSOVER PROBABILITY = 0.7, MUTATION PROBABILITY = 0.3 COIN: K = 0.1
Research in Computing
• Current exciting topics– brain science research– graphics processing unit– low power computing– social network– Google traffic report
Future Trends
• DuPont budget US$ 1.4 BN for R&D– Increase food
production 50%– Reduce dependence
on fossil fuel 15%– Life protection 12%– Emerging markets
23%
food production
reduce fossil fuel
life protection
emerging markets
Research for Thais
• Agriculture: Improving agricultural product
• Healthcare: Thai digital medical record
• Politics: Vote through mobile phone
David Patterson's Six Steps
• Selecting a problem
• Picking a solution
• Running a project
• Finishing a project
• Quantitative evaluation
• Transferrring technology
• let Beauty leads Science
• let Science leads Education
• Eamsiri, J., Malasit, P., Songsivilai, S., Chongstitvatana, P., "Intelligent tutor program in medical teaching", Proc. of the regional symp on computer science and its applications, Bangkok, 1987.
• Wongsamethin, O., Kienprasit, R. and Chongstitvatana, P., "Fast Fourier Transform by a Digital Signal Processor", 10th Electrical Engineering Conference, Thailand, 1987.
• Chongstitvatana, P., "Vision-based behavioural modules for robotic assembly system", IEEE Inter. Conf. on Tools with Artificial Intelligence, New Orleans, 1994, pp.312-316.
• Manovit, C., Aporntewan, C., and Chongstitvatana, P., "Synthesis of Synchronous Sequential Logic Circuits from Partial Input/Output Sequences", Proc. of 2nd Int. Conf. on Evolvable Systems (ICES98), Lausanne, Switzerland, 1998, pp. 98-105.
• Aporntewan, C. and Chongstitvatana, P., "A Hardware Implementation of the Compact Genetic Algorithm", IEEE Congress on Evolutionary Computation, Seoul, Korea, May 27-30, 2001, pp.624-629.
• Aporntewan, C. and Chongstitvatana, P., "Building block identification by simulateneity matrix for hierarchical problems", Genetic and Evolutionary Computation Conference, Seattle, USA, 26-30 June 2004, Proc. part 1, pp.877-888.
• Aporntewan, C., Chongstitvatana, P., "Building-block identification by simultaneity matrix". Soft Computing, Vol.11, No.6, 2007, pp.541-548.
• Rimcharoen, S., Sutivong, D., Chongstitvatana, P., "Real options approach to evaluating genetic algorithms," Applied Soft Computing, Vol 9, Issue 3, June 2009, Pages 896-905.
• Wattanapornprom, W. and Chongstitvatana, P., "Multi-objective Combinatorial Optimisation with Coincidence Algorithm," IEEE Congress on Evolutionary Computation, Norway, May 18-21, 2009.
• Chedtha Puncreobutr, Gobboon Lohthongkum, Prabhas Chongstitvattana, Boonrat Lohwongwatana,"Modeling of Reflow Temperatures and Wettability in Lead-free Solder Alloys using Hybrid Evolutionary Algorithms," Symp of Pb-Free Solders and Emerging Interconnect and Packaging Technologies (TMS 2010), February 14-18, 2010, Seattle, USA.
Teamwork