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Soft Computing
Erschienen in: Soft Computing 6/2012

01.06.2012 | Original Paper

Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks

verfasst von: Rohitash Chandra, Marcus Frean, Mengjie Zhang

Erschienen in: Soft Computing | Ausgabe 6/2012

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Abstract

Adaptation during evolution has been an important focus of research in training neural networks. Cooperative coevolution has played a significant role in improving standard evolution of neural networks by organizing the training problem into modules and independently solving them. The number of modules required to represent a neural network is critical to the success of evolution. This paper proposes a framework for the adaptation of the number of modules during evolution. The framework is called adaptive modularity cooperative coevolution. It is used for training recurrent neural networks on grammatical inference problems. The results shows that the proposed approach performs better than its counterparts as the dimensionality of the problem increases.
Vorheriger Artikel Design of particle-reinforced polyurethane mould materials for soft tooling process using evolutionary multi-objective optimization algorithms
Nächster Artikel I-convergence in probabilistic n-normed space

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Literatur
Blanco A, Delgado M, Pegalajar MC (2001) A real-coded genetic algorithm for training recurrent neural networks. Neural Netw 14(1):93–105MathSciNetCrossRef
Brookshear JG (1989) Theory of computation: formal languages, automata, and complexity. Benjamin-Cummings Publishing Co., Inc., Redwood CityMATH
Castao MA, Vidal E, Casacuberta F (1995) Finite state automata and connectionist machines: a survey. In: Mira J, Hernndez FS (eds) IWANN. Lecture Notes in Computer Science. Springer, New York, pp 433–440
Chandra R, Frean M, Zhang M (2010) An encoding scheme for cooperative coevolutionary feedforward neural networks. In: Li J (ed) AI 2010: advances in artificial intelligence. Lecture Notes in Computer Science, vol 6464. Springer, Berlin, pp 253–262
Chandra R, Frean M, Zhang M (2011a) Modularity adaptation in cooperative coevolutionary feedforward neural networks. In: International joint conference on neural networks, pp 681–688
Chandra R, Frean M, Zhang M, Omlin CW (2011b) Encoding subcomponents in cooperative co-evolutionary recurrent neural networks. Neurocomputing 74(17):3223–3234CrossRef
Chandra R, Omlin CW (2006) Training and extraction of fuzzy finite state automata in recurrent neural networks. In: Proceedings of international conference on computational intelligence, pp 274–279
Chen W, Weise T, Yang Z, Tang K (2010) Large-scale global optimization using cooperative coevolution with variable interaction learning. In: Proceedings of the 11th international conference on Parallel problem solving from nature: Part II. PPSN’10, pp 300–309
Deb K, Anand A, Joshi D (2002) A computationally efficient evolutionary algorithm for real-parameter optimization. Evol Comput 10(4):371–395CrossRef
Gabrijel I, Dobnikar A (2003) On-line identification and reconstruction of finite automata with generalized recurrent neural networks. Neural Netw 16(1):101–120CrossRef
García-Pedrajas N, Ortiz-Boyer D (2007) A cooperative constructive method for neural networks for pattern recognition. Pattern Recogn 40(1):80–98MATHCrossRef
Giles CL, Lawrence S, Tsoi A (1997) Rule inference for financial prediction using recurrent neural networks. In: Proceedings of the conference on computational intelligence for financial engineering, pp 253–259
Giles CL, Omlin C, Thornber KK (1999) Equivalence in knowledge representation: automata, recurrent neural networks, and dynamical fuzzy systems. Proc IEEE 87(9):1623–1640CrossRef
Gomez F, Mikkulainen R (1997) Incremental evolution of complex general behavior. Adapt Behav 5(3–4):317–342CrossRef
Gomez F, Schmidhuber J, Miikkulainen R (2008) Accelerated neural evolution through cooperatively coevolved synapses. J Mach Learn Res 9:937–965MathSciNetMATH
Gomez FJ (2003) Robust non-linear control through neuroevolution. Technical Report AI-TR-03-303, PhD thesis, Department of Computer Science, The University of Texas at Austin
Haykin S, Principe J, Sejnowski T, McWhirter J (2006) New directions in statistical signal processing: from systems to brain. MIT Press, Cambridge
Kolen J, Kremer S (2001) A field guide to dynamical recurrent networks. IEEE Press, New Jersey
Li X, Yao X (2011) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evol Comput (in press)
Liu Y, Yao X, Zhao Q, Higuchi T (2001) Scaling up fast evolutionary programming with cooperative coevolution. In: Proceedings of the 2001 Congress on Evolutionary Computation, pp 1101–1108
Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evol Comput 12(3):273–302CrossRef
Manolios P, Fanelli R (1994) First-order recurrent neural networks and deterministic finite state automata. Neural Comput 6(6):1155–1173CrossRef
Marakami K, Taguchi H (1991) Gesture recognition using recurrent neural networks. In: Proceedings of the SIGCHI conference on Human factors in computing systems: reaching through technology, pp 237–242
Medsker L, Jain L (1999) Recurrent neural networks: design and application, Computer Intelligence. CRC Press international
Omidvar M, Li X, Yao X (2010) Cooperative co-evolution for large scale optimization through more frequent random grouping. In: 2010 IEEE Congress on evolutionary computation (CEC), pp 1754–1761
Omidvar MN, Li X, Yao X (2011) Smart use of computational resources based on contribution for cooperative co-evolutionary algorithms. In: Proceedings of the 13th annual conference on genetic and evolutionary computation. GECCO ’11, pp 1115–1122
Omlin CW, Thornber KK, Giles CL (1998) Fuzzy finite state automata can be deterministically encoded into recurrent neural networks. IEEE Trans Fuzzy Syst 6:76–89CrossRef
Ong Y-S, Lim M-H, Zhu N, Wong K-W (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst ManCybern Part B Cybern 36(1):141–152CrossRef
Ortiz-Boyer D, HerváMartínez C, García-Pedrajas N (2005) Cixl2: a crossover operator for evolutionary algorithms based on population features. J Artif Int Res 24:1–48MATHCrossRef
Potter MA, De Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29CrossRef
Potter MA, Jong KAD (1994) A cooperative coevolutionary approach to function optimization. In: PPSN III: proceedings of the international conference on evolutionary computation. The third conference on parallel problem solving from nature. Springer, London, UK, pp 249–257
Robinson T (1994) An application of recurrent nets to phone probability estimation. IEEE Trans Neural Netw 5:298–305CrossRef
Salomon R (1996) Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. Biosystems 39(3):263–278CrossRef
Schmidhuber J, Wierstra D, Gagliolo M, Gomez F (2007) Training recurrent networks by evolino. Neural Comput 19(3):757–779CrossRef
Shi Y-j, Teng H-f, Li Z-Q (2005) Cooperative co-evolutionary differential evolution for function optimization. In: Wang L, Chen K, Ong YS (eds) Advances in natural computation. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, pp 1080–1088
Smith J (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern Part B Cybern 37(1):6–17CrossRef
Tomita M (1982) Dynamic construction of finite automata from examples using hill-climbing. In: Proceedings of the fourth annual cognitive science conference. Ann Arbor Michigan, pp 105–108
van den Bergh F, Engelbrecht A (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239CrossRef
Watrous RL, Kuhn GM (1992) Induction of finite-state languages using second-order recurrent networks. Neural Comput 4(3):406–414CrossRef
Yang Z, Tang K, Yao X (2008a) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999MathSciNetCrossRef
Yang Z, Tang K, Yao X (2008b) Multilevel cooperative coevolution for large scale optimization. In: IEEE congress on evolutionary computation, pp 1663–1670
Metadaten
Titel
Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks
verfasst von
Rohitash Chandra
Marcus Frean
Mengjie Zhang
Publikationsdatum
01.06.2012
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 6/2012
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-011-0798-9

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