# Scaling up Evolutionary Algorithms for Large Data Sets

Chapter

## Abstract

One well-known disadvantage of evolutionary algorithms (EAs) for rule discovery is that in general they are slow, by comparison with rule discovery algorithms based on the rule induction paradigm. After all, rule induction algorithms usually perform a kind of local search in the rule space, whereas EAs are population-based algorithms that perform a more global search of the rule space.

## Keywords

Local Memory Fitness Evaluation Training Instance Fitness Computation Rule Discovery
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

## Preview

Unable to display preview. Download preview PDF.

## References

- [Almasi and Gottlieb 1994]G.S. Almasi and A. Gottlieb.
*Highly Parallel Computing*, 2nd edn. The Benjamim Cummings, 1994.Google Scholar - [Anglano et al. 1997]C. Anglano, A. Giordana, G. Lo Bello and L. Saitta.
*A network genetic algorithm for concept learning*. Proceedings of the 7th International Conference Genetic Algorithms, 434–441. 1997.Google Scholar - [Anglano et al. 1998]C. Anglano, A. Giordana, G. Lo Bello and L. Saitta. An experimental evaluation of coevolutive concept learning.
*Machine Learning: Proceedings of the 15th International Conference (ICML ‘88)*, 19–27. Morgan Kaufmann, 1998.Google Scholar - [Araujo et al. 1999]D.L.A. Araujo, H.S. Lopes and A.A. Freitas. A parallel genetic algorithm for rule discovery in large databases.
*Proceedings of the 1999 IEEE Systems*, Man and Cybernetics Conference, v. III, 940–945. Tokyo, 1999.Google Scholar - [Araujo et al. 2000]D.L.A. Araujo, H.S. Lopes and A.A. Freitas. Rule discovery with a parallel genetic algorithm. In: A. Wu (Ed.)
*Proceedings of the 2000 Genetic and Evolutionary Computation Conference (GECCO ‘2000) Workshop Program — Workshop on Data Mining with Evolutionary Algorithms*, 89–92. Las Vegas, NV, USA. 2000.Google Scholar - [Bhattacharrya 1998]S. Bhattacharrya. Direct marketing response models using genetic algorithms. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD ‘88), 144–148. AAAI Press, 1998.Google Scholar
- [Cantu-Paz 2000]E. Cantu-Paz.
*Efficient and Accurate Parallel Genetic Algorithms*. Kluwer, 2000.Google Scholar - [Cavaretta and Chellapilla 1999]M.J. Cavaretta and K. Challapilla. Data mining using genetic programming: the implications of parsimony on generalization error.
*Proceedings of the Congress on Evolutionary Computation (CEC ‘89)*, 1330–1337. IEEE, 1999.Google Scholar - [Flockhart and Radcliffe 1995]I.W. Flockhart and N.J. Radcliffe. GA-MINER: parallel data mining with hierarchical genetic algorithms - final report.
*EPCCAIKMS-GA-MINER-Report 1.0*. University of Edinburgh, UK, 1995.Google Scholar - [Freitas 1997]A.A. Freitas. A genetic programming framework for two data mining tasks: classification and generalized rule induction.
*Genetic Programming 1997: Proceedings of the 2nd Annual Conference (GP ‘87)*, 96–101. Morgan Kaufmann, 1997.Google Scholar - [Freitas 1998]A.A. Freitas. A survey of parallel data mining.
*Proceedings of the 2nd International Conference on the Practical Applications of Knowledge Discovery and Data Mining (PADD ‘88)*, 287–300. The Practical Application Company, London, 1998.Google Scholar - [Freitas and Lavington 1998]A.A. Freitas and S.H. Lavington.
*Mining Very Large Databases with Parallel Processing*. Kluwer, 1998.Google Scholar - [Gathercole and Ross 1994]C. Gathercole and P. Ross. Dynamic training subset selection for supervised learning in genetic programming.
*Parallel Problem Solving from Nature (PPSN-III)*, 312–321. Springer, 1994.CrossRefGoogle Scholar - [Gathercole and Ross 1997a]C. Gathercole and P. Ross. Tackling the boolean even N parity problem with genetic programming and limited-error fitness.
*Genetic Programming 1997: Proceedings of the 2nd Annual Conference (GP ‘87)*, 119–127. Morgan Kaufmann, 1997.Google Scholar - [Gathercole and Ross 1997b]C. Gathercole and P. Ross. Small populations over many generations can beat large populations over few generations in genetic programming.
*Genetic Programming 1997: Proceedings of the 2nd Annual Conference (GP ‘87)*, 111–118. Morgan Kaufmann, 1997.Google Scholar - [Giordana and Neri 1995]A. Giordana and F. Neri. Search-intensive concept induction.
*Evolutionary Computation 3(4)*: 375–416, 1995.CrossRefGoogle Scholar - [Goldberg 1989]D.E. Goldberg.
*Genetic Algorithms in Search*, Optimization and Machine Learning. Addison-Wesley, 1989.Google Scholar - [Hillis and Steele 1986]W.D. Hillis and L. Steele Jr.
*Data parallel algorithms. Communications of the ACM*, 29 (12), 1170–1183, 1986.Google Scholar - [Lee et al. 2000]C.-H. Lee, S.-H. Park and J.-H. Kim. Topology and migration policy of fine-grained parallel evolutionary algorithms for numerical optimi-zation.
*Proceedings of the 2000 Congress on Evolutionary Computation (CEC ‘2000)*. IEEE, 2000.Google Scholar - [Lewis 1991]T.G. Lewis. Data parallel computing: an alternative for the 1990s.
*IEEE Computer*, 24 (9), 110–111, 1991.Google Scholar - [Llora and Garrel 2001a]X. Llora and J.M. Garrell. Knowledge-independent data mining with fine-grained parallel evolutionary algorithms.
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ‘2001)*, 461–468. Morgan Kaufmann, 2001.Google Scholar - Llora and Garrel 2001b] Inducing partially-defined instances with evolutionary algorithms.
*Proceedings of the 18th International Conference on Machine Learning (ICML ‘2001)*, 337–344. Morgan Kaufmann, 2001.Google Scholar - [Neri and Giordana 1995]F. Neri and A. Giordana. A parallel genetic algorithm for concept learning.
*Proceedings of the 6th International Conference Genetic Algorithms*, 436–443. 1995.Google Scholar - [Neri and Saitta 1996]F. Neri and L. Saitta. Exploring the power of genetic search in learning symbolic classifiers.
*IEEE Transactions on Pattern Analysis and Machine Intelligence*, 18 (11), 1135–1141, 1996.CrossRefGoogle Scholar - [Papagelis and Kalles 2001]A. Papagelis and D. Kalles. Breeding decision trees using evolutionary techniques.
*Proceedings of the 18th International Conference on Machine Learning (ICML ‘2001)*, 393–400. Morgan Kaufmann, 2001.Google Scholar - [Sharpe and Glover 1999]P.K. Sharpe and R.P. Glover. Efficient GA based techniques for classification.
*Applied Intelligence**11*, 277–284, 1999.CrossRefGoogle Scholar - [Teller and Andre 1997]A. Teller and D. Andre. Automatically choosing the number of fitness cases: the rational allocation of trials.
*Genetic Programming 1997: Proceedings of the 2nd Annual Conference (GP ‘87)*, 321–328. Morgan Kaufmann, 1997.Google Scholar

## Copyright information

© Springer-Verlag Berlin Heidelberg 2002