Real-Valued Constraint Optimization with ICHEA

1. Amirjanov, A.: The development of a changing range genetic algorithm. Computer Methods in Applied Mechanics and Engineering 195(19-22), 2495–2508 (2006)

   Article  MathSciNet  MATH  Google Scholar 

2. Back, T., et al. (eds.): Handbook of Evolutionary Computation. IOP Publishing Ltd. (1997)

   Google Scholar 

3. Brailsford, S.: Constraint satisfaction problems: Algorithms and applications. European Journal of Operational Research 119(3), 557–581 (1999)

   Article  MATH  Google Scholar 

4. Coello Coello, C.A.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering 191(11-12), 1245–1287 (2002)

   Article  MathSciNet  MATH  Google Scholar 

5. Craenen, B.G.W.: Solving constraint satisfaction problems with evolutionary algorithms. Phd Dissertation, Vrije Universiteit (2005)

   Google Scholar 

6. Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

   Article  MathSciNet  Google Scholar 

7. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43 (1995)

   Google Scholar 

8. Eiben, A.E.: Evolutionary algorithms and constraint satisfaction: definitions, survey, methodology, and research directions. Presented at the (2001)

   Google Scholar 

9. Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423. Morgan Kaufmann Publishers Inc., San Francisco (1993)

   Google Scholar 

10. Onwubolu, G.C., Sharma, A.: Particle Swarm Optimization for the assignment of facilities to locations. In: New Optimization Techniques in Engineering. Springer (2004)

    Google Scholar 

11. Liang, J.J., et al.: Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-parameter Optimization. Nanyang Technological University, Singapore (2006)

    Google Scholar 

12. Liu, H., et al.: Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl. Soft Comput., 629–640 (2010)

    Google Scholar 

13. Mezura-montes, E., Coello, C.A.C.: A Survey of Constraint-Handling Techniques Based on Evolutionary Multiobjective Optimization. Departamento de Computación, Evolutionary Computation Group at CINVESTAV (2006)

    Google Scholar 

14. Mezura-Montes, E., Coello, C.A.C.: A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation 9(1), 1–17 (2005)

    Article  Google Scholar 

15. Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation 4(1), 1–32 (1996)

    Article  Google Scholar 

16. Müller, T.: Constraint-based Timetabling. PhD Dissertation, Charles University (2005)

    Google Scholar 

17. Paredis, J.: Co-evolutionary Constraint Satisfaction. In: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature, pp. 46–55. Springer (1994)

    Google Scholar 

18. Becerra, R.L., Coello Coello, C.A.: Cultured differential evolution for constrained optimization. In: Computer Methods in Applied Mechanics and Engineering, vol. 195(33-36), pp. 4303–4322 (2006)

    Google Scholar 

19. Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. Erlbaum Associates Inc. (1985)

    Google Scholar 

20. Sharma, A., Sharma, D.: ICHEA – A Constraint Guided Search for Improving Evolutionary Algorithms. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part I. LNCS, vol. 7663, pp. 269–279. Springer, Heidelberg (2012)

    Google Scholar 

21. Tessema, B., Yen, G.G.: A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 246–253. IEEE (2006)

    Google Scholar 

22. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)

    Article  Google Scholar 

Download references