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Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia.

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Presentation on theme: "Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia."— Presentation transcript:

1 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk USING GENETIC ALGORITHM WITH ADAPTIVE MUTATION MECHANISM FOR NEURAL NETWORKS DESIGN AND TRAINING Yuri R. Tsoy, Vladimir G. Spitsyn, Department of Computer Engineering Tomsk Polytechnic University

2 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk Report contents 1. Introduction 2. Description of the algorithm 3. Adaptive mutation mechanism 4. Results of experiments 5. Implementation 6. Conclusion

3 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 1.Genetic algorithms and neural networks Genetic algorithms (GAs) use evolutionary concept (heredity, mutability and natural selection) to solve optimization tasks. The idea of Artificial neural networks (ANNs) is inspired by functionality of human brain. ANNs are often used to solve classification and approximation tasks

4 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 1. Use of neural networks Preparation of Training Data Definition of Structure (Design) Tuning of Weights (Training) ++ Genetic Algortihm Neuroevolution

5 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 2. Description of the algorithm NEvA – NeuroEvolutionary Algorithm 1. Simultaneous design and tuning of neural network 2. 1 individual = 1 neural network 3. Population = set of neural networks

6 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 2. Description of the algorithm Initial population: Networks without hidden neurons. Weights of connections are encoded with 19-bit within range [ -26,2144; 26,2144 ] with precision 0,0001. Growth of the complexity of networks during the process of evolution. Neurons with log-sigmoid activation function:

7 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 3. Adaptive mechanism of mutation Types of mutation: Addition of random connection Deletion of random connection Addition of random neuron Deletion of random neuron Change of weight of random connection

8 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 3. Adaptive mechanism of mutation Rnd > F C Delete random neuron Add random connection Rnd > F C N H > 0 no Rnd > F N yes no Delete random connection Add random neuron Rnd > F C & N H > 0 yes no yes no f C percentage of implemented connections f N depends on number of hidden neurons F C = f C 2 F N = F C * f N 2 Rnd is a random number N H is a number of hidden neurons

9 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 3. Adaptive mechanism of mutation x1x1 x2x2 y1y1 p1p1 p2p2 p3p3p4p4 Rnd > F C Delete random neuron Add random connection Rnd > F C N H > 0 no Rnd > F N yes no Delete random connection Add random neuron Rnd > F C & N H > 0 yes no yes no f C = 6 /(0,5*(5*4 – 2*1 – 1*0)) = 0,667 f N = 3/5 = 0,6 F C = f C 2 = 0,445, F N = F C * f N 2 = 0,160 p1 = ( 1 - F C )(1-F C +F C ) = 0,308 p2 = 0,454, p3 = 0,071, p4 = 0,166 Example:

10 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk N C – number of connections in neural network represented by individual. Mutation event is “gambled” N I +N O times for each individual, where N I and N O – number of inputs and outputs in networks respectively. Probability and type of mutation are defined individually for each network. 3. Adaptive mechanism of mutation Probability of mutation: “Decrease of probability of mutation during the evolution improves performance of the genetic algorithm” (Schаffer, Caruana, Eshelman, Das, 1989, Goldberg, 1989, Eiben, Hinterding, Michalewicz,1999, Igel, Kreutz, 2003 )

11 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 4. Results of experiments Exclusive OR Average number of object function calculations NHNH NCNC Population size NEAT147552,357,48150 NEAT266123,1211,72150 CGA13165, BP5338, BPM828, NEvA (no adaptive P m ) 6026,264,816,2213 NEvA (19-bit)6063,44,7815,8613

12 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 4. Results of experiments 1 pole balancing Number of trials NHNH NCNC MeanBestWorst GENITOR SANE ESP NEvA (no adaptive P m ) ,585,22 NEvA (19-bit)358, ,186,56,5

13 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 4. Results of experiments 2 poles balancing Average number of trials Population size NHNH NCNC Evolutionary programming N/A SANE N/A ESP NEAT N/A (0–4) N/A (6–15) NEvA (no adaptive P m ) ,47,24 NEvA (19-bits)1338,92320,587,27,2

14 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 4. Future plans “Parameterless” variant of NEvA with adaptive mutation mechanism and adaptive population sizing. Some preliminary results: XOR (N H : N C ) 1-pole balancing (N H : N C ) 2-pole balancing (N H : N C ) NEvA (adaptive mutation) 6063,4 (4,78 : 15,86) 358,2 (1,18 : 6,5) 1338,92 (0,58 : 7,22) NEvA (adaptive mutation + adaptive population sizing) 6608,84 (5,14 : 17,26) 634,22 (1,26 : 6,74) 1464,54 (0,62 : 7,32)

15 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk 5. Implementation The introduced algorithm is implemented to comply with the architecture of the software environment “GA Workshop” General scheme of “GA Workshop”

16 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk At the time “GA Workshop” is under construction (there is no GUI), although it is already possible to use it for researches. The following researches have been done: Experiments with NEvA. Study of quasi-species model by M. Eugen. Study of majoring model by V.G. Red’ko. Investigation of simple population sizing techniques. Numerical optimization with use genetic algorithm. Experiments with compensatory genetic algorithm. 5. “GA Workshop”

17 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk Features: 3 different variants of genetic algorithm, including “standard” GA, compensatory GA and NEvA; 5 different crossover operators for the binary encoded GA and 4 different crossover operators for NEvA; 4 selection strategies; 13 benchmark problems; 3 different population sizing strategies; 5. “GA Workshop”

18 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk Data available for analysis: Data that describes each generation of GA (fitness distribution for each generation averaged over multiple launches). Data that describes GA behavior (dynamics of averaged mean, the best and the worst fitness value for each generation, dynamics of averaged deviation of fitness, time per launch in milliseconds). Data that describes obtained solutions (number of object function calculations until solution is found, time until the first solution is found in milliseconds. All solutions and some data describing additional properties of the solutions are output into separate file for further analysis and use) 5. “GA Workshop”

19 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk Conclusion Results of experiments showed that NEvA performance is comparable and in some cases surpass results of other algorithms for the reviewed problems (XOR problem and full information pole balancing) Adaptive mutation rate caused increase of performance in comparison with NEvA with fixed Pm (up to 40% for 2-pole balancing task), although resulting networks were slightly worse for 1-pole balancing problems.

20 Yuri R. Tsoy, Vladimir G. Spitsyn, «Using genetic algorithms with adaptive mutation mechanism for neural networks design and training» 9th Korea-Russia International Symposium on Science and Technology - KORUS 2005 June 26 - July 2, 2005, Novosibirsk State Technical University, Novosibirsk Thank you for your attention!


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