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