Embedded Algorithm in Hardware: A Scalable Compact Genetic Algorithm Prabhas Chongstitvatana Chulalongkorn University
What is Genetic Algorithms GA is a probabilistic search procedure to obtain solutions starting from a set of candidate solutions, using improving operators to “evolve” solutions. Improving operators are inspired by natural evolution.
Characteristics of GA Survival of the fittest. The objective function depends on the problem. GA is not a random search.
GA pseudo code GA initialise population P while not terminate evaluate P by fitness function P' = selection recombination mutation P P = P‘ terminating conditions: 1. found satisfactory solutions 2. waiting too long
Simple Genetic Algorithm represent a solution by binary string {0,1}* selection: chance to be selected is proportional to its fitness recombination single point crossover
Genetic Operators recombination select a cut point cut two parents, exchange parts AAAAAA AA AAAA cut at bit 2 AA AAAA exchange parts mutation single bit flip > flip at bit 4
What problem GA is good for? Highly multimodal functions Discrete or discontinuous functions High-dimensionality functions, including many combinatorial ones Nonlinear dependencies on parameters (interactions among parameters) -- “epistasis” Often used for approximating solutions to NPcomplete
Thai stock exchange prediction January 2003 – December 2004
Controller of a 7 DOF Bibed
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)
Scalable Compact Genetic Algorithm in Hardware Jewajinda, Y. and Chongstitvatana, P. 2006
Pseudocode of the normal CoCGA cell 1.Generate two individual from the vector 2.Let them compete 3.Update the probability vector toward better one and lncrement Confidence Counter 4.Check if cc is incremented then Send p and cc to the group leader cell 5.Check if the vector has converged else goto step 1 6.probability vector represents the final solution
Pseudocode of the group leader 1.Check if cc of each neighbor is updated 2.Select the highest cc of all neighbors 3.Update p, with pcc with ccmax 4.Update new updated p, to all normal Cell for each neighbor cell of leader cells 5.Check if the vector has converged else goto step 1 6.p, represents the final solution
A Block of CGA cell
CoCGA with two neighbors
Speedup ONEMAXF1F2F3 CGA CoCGA speedup SPEEDUP COMPARISON BETWEEN CGA AND COCGA IN TERM OF MACHINE CYCLES (ONE MACHINE CYCLE IS EQUIVALENT To FOUR CLOCK CYCLES)
References Jewajinda, Y. and Chongstitvatana, P., "FPGA-based Online-learning using Parallel Genetic Algorithm and Neural Network for ECG Signal Classification," Proc. of ECTI Conf., May 2010, Chiengmai, Thailand. (Best paper award) Jewajinda, Y. and Chongstitvatana, P.,"FPGA Implementation of a Cellular Univariate Estimation of Distribution Algorithm and Block-based Neural Network as an Evolvable Hardware", IEEE Congress on Evolutionary Computation, Hong Kong, June 1-6, 2008, pp Jewajinda, Y. and Chongstitvatana, P., "A Cooperative Approach to Compact Genetic Algorithm for Evolvable Hardware", IEEE World Congress on Computational Intelligence, Vancouver, Canada, July 16-21, 2006, pp Niparnan, N. and Chongstitvatana, P., "An improved genetic algorithm for the inference of finite state machine", IEEE Int. Conf. on Systems, Man and Cybernetics, Vol.7, 2002, pp , Tunisia, 6-9 Oct, 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 Aporntewan, C., and Chongstitvatana, P., "An on-line evolvable hardware for learning finite-state machine", Proc. of Int. Conf. on Intelligent Technologies, Bangkok, December 13-15, 2000, pp
Teamwork