Theoretical and Experimental Study of Crossover Operators of Genetic Algorithms

Journal of Optimization Theory and Applications

Volumn 99, Issue 2, 1998, Pages 271-302

  Belfiore, N P ^a   Esposito, A ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Binary encoding; Convergence; Crossover operators; Genetic algorithms; Real number based problems; Real number encoding

Indexed keywords

ENCODING (SYMBOLS); SIGNAL ENCODING;

BINARY CODED GENETIC ALGORITHMS; BINARY ENCODINGS; CONVERGENCE; CROSS-OVER; CROSSOVER OPERATOR; PROBLEM-BASED; REAL NUMBER; REAL-NUMBER BASED PROBLEM; REAL-NUMBERS ENCODING; THEORETICAL STUDY;

GENETIC ALGORITHMS;

EID: 0032356395     PISSN: 00223239     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1021766025497     Document Type: Article

References (11)

1. HEITKOETTER, J., and BEASLEY, D., Editors, The Hitch-Hiker's Guide to Evolutionary Computation: A List of FAQ, USENET: comp.ai.genetic; available via anonymous FTP from rtfm.mit.edu:/pub/usenet/news.answers/ai-faq/ genetic/, about 90 pages, 1997.
2. GOLDBERG, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, Reading, Massachusetts, 1989.
3. DAVIS, L., Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, New York, 1991.
4. MICHALEWICZ, Z., Genetic Algorithsm + Data Structures = Evolution Programs, Springer Verlag, 2nd Edition, Berlin, Germany, 1994.
5. GEN, M., and CHENG, R., Genetic Algorithms and Engineering Design, John Wiley and Sons, New York, New York, 1997.
6. BELFIORE, N. P., Di BENEDETTO, A., and ESPOSITO, A., An Evolutionary Approach to Kinematic Synthesis of Articulated Plane Mechanisms, Proceedings of the 13th Italian Conference on Theoretical and Applied Mechanics, AIMETA, Siena, Italy, Volume 2, pp. 105-110, 1997 (in Italian).
7. CALANDRELLI, L., and BELFIORE, N. P., A New Approach to Wear Test Designing for Leg Mechanism Hip Joints, Proceedings of the 6th International Workshop on Robotics in the Alpe-Danube Region, Cassino, Italy, pp. 547-552, 1997.
8. HOLLAND, J. H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Michigan, 1975.
9. ESCHELMAN, L., and SCHAFFER, J., Real-Coded Genetic Algorithms and Interval Schemata, Foundations of Genetic Algorithms 2, Edited by L. Whitley, Morgan Kaufmann Publishers, San Mateo, California, pp. 187-202, 1993.
10. MÜHLENBEIN, H., and SCHLIERKAMP-VOOSEN, D., Predictive Models for the Breeder Genetic Algorithm, Part 1: Continuous Parameter Optimization, Evolutionary Computation, Vol. 1, pp. 25-49, 1993.
11. SEFRIOUI, M., PÉRIAUX, J., and GANASCIA, J. G., Fast Convergence Thanks to Diversity, Evolution Programming V : Proceedings of the 5th Annual Conference on Evolutionary Programming, Edited by L. J. Fogel, P. J. Angeline, and T. Baeck, Bradford Book, MIT Press, Cambridge, Massachusetts, pp. 313-321, 1995.