Credit Assignment in Adaptive Evolutionary Algorithms

Whitacre, Dr James M and Pham, Dr Tuan Q and Sarker, Dr Ruhul A (2006) Credit Assignment in Adaptive Evolutionary Algorithms. [Conference Paper]

Full text available as:

PDF - Accepted Version


In this paper, a new method for assigning credit to search operators is presented. Starting with the principle of optimizing search bias, search operators are selected based on an ability to create solutions that are historically linked to future generations. Using a novel framework for defining performance measurements, distributing credit for performance, and the statistical interpretation of this credit, a new adaptive method is developed and shown to outperform a variety of adaptive and non-adaptive competitors.

Item Type:Conference Paper
Keywords:Evolutionary Algorithm, Genetic Algorithm, Adaptation, Historical Credit Assignment, Search Bias
Subjects:Computer Science > Artificial Intelligence
ID Code:6580
Deposited By: Whitacre, Dr James M
Deposited On:06 Jul 2009 09:42
Last Modified:11 Mar 2011 08:57

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

[1] Barbosa, H. J. C. and e Sá, A. M. On Adaptive Operator

Probabilities in Real Coded Genetic Algorithms, In

Workshop on Advances and Trends in Artificial Intelligence

for Problem Solving (SCCC '00), (Santiago, Chile,

November 2000).

[2] Davis, L. Handbook of Genetic Algorithms, van Nostrand

Reinhold, New York, 1991.

[3] De Jong, K. An analysis of the behaviour of a class of

genetic adaptive systems. Ph. D Thesis, University of

Michigan, Ann Arbor, Michigan, 1975.

[4] Herrera, F. and Lozano, M. Tackling real-coded genetic

algorithms: Operators and tools for the behavioural analysis,

Artificial Intelligence Review 12, 4, (1998), 265-319.

[5] Herrera, F., Lozano, M., and Sánchez, A. M. 2005. Hybrid

crossover operators for real-coded genetic algorithms: an

experimental study. Soft Comput. 9, 4 (Apr. 2005), 280-298.

[6] Janka, E. Vergleich stochastischer Verfahren zur globalen

Optimierung, Diploma Thesis, University of Vienna, Vienna,

Austria, 1999.

[7] Julstrom, B. A. Adaptive operator probabilities in a genetic

algorithm that applies three operators. In Proceedings of the

1997 ACM Symposium on Applied Computing (SAC '97)

(San Jose, California, United States). ACM Press, New

York, NY, 233-238, 1997.

[8] Muhlenbein, H., Schomisch, M. and Born, J. The parallel

genetic algorithm as function optimizer. In Proc. of 4th

International Conference of Genetic Algorithms, 271-278,


[9] Pham, Q.T. Dynamic Optimization of Chemical Engineering

Processes by an Evolutionary Method. Comp. Chem. Eng.,

22 (1998), 1089-1097.

[10] Pham, Q. T. Competitive evolution: a natural approach to

operator selection. In: Progress in Evolutionary

Computation, Lecture Notes in Artificial Intelligence,

(Evolutionary Computation Workshop) (Armidale, Australia,

November 21-22, 1994). Springer-Verlag, Heidelberg, 1995,


[11] Storn, R. and Price, K. Differential Evolution - A Simple and

Efficient Adaptive Scheme for Global Optimization over

Continuous Spaces. Technical Report TR-95-012,

International Computer Science Institute, Berkeley, CA,


[12] Whitacre, J., Pham, Q.T., Sarker, R. Use of Statistical

Outlier Detection Method in Adaptive Evolutionary

Algorithms. In Proceedings of the 2006 Conference on

Genetic and Evolutionary Computation (GECCO '05)

(Seattle, USA, July 8-12, 2006). ACM Press, New York, NY,



Repository Staff Only: item control page