Improving prediction in evolutionary algorithms for dynamic environments

Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Volumn , Issue , 2009, Pages 875-882

  Simões, Anabela ^a   Costa, Ernesto ^b  

^a POLYTECHNIC INSTITUTE OF COIMBRA   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

Dynamic environments; Evolutionary algorithms; Linear regression; Markov chains; Prediction

Indexed keywords

DYNAMIC ENVIRONMENTS; LINEAR PREDICTORS; MARKOV CHAIN; MARKOV CHAIN MODELS; NONLINEAR CHANGES;

MARKOV PROCESSES; REGRESSION ANALYSIS;

EVOLUTIONARY ALGORITHMS;

EID: 72749090355     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1570021     Document Type: Conference Paper

References (18)

1. P. A. Bosman. Learning, Anticipation and Time-deception in Evolutionary Online Dynamic Optimization. In S. Yang and J. Branke, editors, GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization, 2005.
2. P. A. Bosman. Learning and Anticipation in Online Dynamic Optimization. In S. Yang, Y.S. Ong and Y. Jin, editors, Evolutionary Computation in Dynamic and Uncertain Environments. Springer-Verlag, 2007.
3. J. Branke. Memory Enhanced Evolutionary Algorithms for Changing Optimization Problems. In IEEE Congress on Evolutionary Computation (CEC 1999), pages 1875-1882. IEEE Press, 1999.
4. J. Branke. Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, 2002.
5. J. Branke, T. Kaußler and C. Schmidt, A Multi-Population Approach to Dynamic Optimization Problems. In I. Parmee, editor, Adaptive Computing in Design and Manufacture (ACDM 2000), pages 299-308. Spriger-Verlag, 2000.
6. J. Branke and D. Mattfeld. Anticipation in dynamic optimization: The scheduling case. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo and H.-P. Schwefel, editors, Parallel Problem Solving from Nature, pages 253-262. Springer, 2000.
7. H. G. Cobb. An Investigation into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms having Continuous, Time-Dependent Nonstationary Environment. Technical Report TR AIC-90-001, Naval Research Laboratory 1990.
8. D. S. Moore and G. P. McCabe. Introduction to the Practice of Statistics (4th edition). Freeman and Company, 2003.
9. Y. Jin and J. Branke. Evolutionary Optimization in uncertain Environments: a survey. IEEE Transactions on Evolutionary Computation, 9(3): 303-317, 2005.
10. C. Rossi, M. Abderrahim and J. C. Díaz. Tracking Moving Optima Using Kalman-Based Predictions. Evolutionary Computation, 16(1): 1-30, MIT Press, 2008.
11. A. Simões and E. Costa. Using Linear Regression to Predict Changes in Evolutionary Algorithms dealing with Dynamic Environments. Technical Report TR 2007/005, ISSN 0874-338X, CISUC, 2007.
12. A. Simões and E. Costa. Variable-size Memory Evolutionary Algorithm to Deal with Dynamic Environments. In M. Giacobini et al., editors, Applications of Evolutionary Computing, volume 4448 of Lecture Notes in Computer Science, pages 617-626. Springer, 2007.
13. A. Simões and E. Costa. Evaluating Prediction's Accuracy in Evolutionary Algorithms for Dynamic Environments. Technical Report TR 2008/04, ISSN 0874-338X, CISUC, 2008.
14. A. Simões and E. Costa. Evolutionary Algorithms for Dynamic Environments: Prediction using Linear Regression and Markov Chains. In Parallel Problem Solving from Nature (PPSN X), pages 306-315. Springer, 2008.
15. P. D. Stroud. Kalman-extended Genetic Algorithm for Search in Nonstationary Environments with Noisy Fitness Evaluations. IEEE Transactions on Evolutionary Computation, 5(1): 66-77, 2001.
16. J. van Hemert, C. Van Hoyweghen, E. Lukshandl and K. Verbeeck. A Futurist Approach to Dynamic Environments. In GECCO EvoDOP Workshop, pages 35-38, 2001.
17. S. Yang. Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments. In S. Yan, Y-S. Ong and Y. Jin, editors, Evolutionary Computation in Dynamic and Uncertain Environments, pages 3-28. Springer-Verlag, 2007.
18. S. Yang and X. Yao. Experimental Study on Population-Based Incremental Learning Algorithms for Dynamic Optimization problems. Soft Computing, 9(11): 815-834, 2005.