1. Abbass, H., Sarker, R., and Newton, C. (2001). PDE: A Pareto Frontier Differential Evolution Approach for Multiobjective Optimisation Problems. In Proceedings of the Congress on Evolutionary Computation 2001, pages 971–978. Seoul, Korea.
2. Ang, K., Li, Y., and Tan, K. C. (2001). Multi-Objective Benchmark Functions and Benchmark Studies for Evolutionary Computation. In Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA’ 2001), pages 132–139, Las Vegas, Nevada.
3. Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York.
4. Carlyle, W. M., Kim, B., Fowler, J. W., and Gel, E. S. (2001). Comparison of Multiple Objective Genetic Algorithms for Parallel Machine Scheduling Problems. In Zitzler, E., Deb, K., Thiele, L., Coello, C. A. C., and Corne, D., editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 472–485. Springer-Verlag. Lecture Notes in Computer Science No. 1993.
5. Caruana, R. and Schaffer, J. D. (1988). Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. In Proceedings of the Fifth International Conference on Machine Learning, pages 132–161, San Mateo, California. Morgan Kauffman Publishers.