Fundamentals of Genetic Algorithms

Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems

Volumn , Issue , 2007, Pages 25-42

  Alves Da Silva, Alexandre P ^a   Falcão, Djalma M ^a  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

Character based and real valued encodings; Problem specific operators definition; Thermal unit commitment

Indexed keywords

EID: 84889432310     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470225868.ch2     Document Type: Chapter

References (35)

1. Fogel DB. An introduction to simulated evolutionary optimization. IEEE Trans Neural Networks 1994; 5(1):3-14.
2. Fogel DB. Evolutionary computation-Toward a new philosophy of machine intelligence. Piscataway, NJ: IEEE Press; 1995.
3. Bäck T, Hammel U, Schwefel H-P. Evolutionary computation: Comments on the history and current state. IEEE Trans Evol Computat 1997; 1:3-17.
4. Goldberg DE. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley; 1989.
5. Beasley D, Bull DR, Martin RR. An overview of genetic algorithms: Part 1, fundamentals. University Computing 1993; 15(2):58-69.
6. Beasley D, Bull DR, Martin RR. An overview of genetic algorithms: Part 2, research topics. University Computing 1993; 15(4):170-181.
7. Whitley D. A genetic algorithm tutorial. Statistics Computing 1994; 4:65-85.
8. Aarts E, Korst J. Simulated annealing and Boltzmann. New York: John Wiley & Sons; 1989.
9. Glover F, Laguna M. Tabu search. Boston: Kluwer Academic; 1997.
10. Kennedy J, Eberhart RC. Swarm intelligence. San Mateo, CA: Morgan Kaufmann; 2001.
11. Rudolph G. Convergence analysis of canonical genetic algorithms. IEEE Trans Neural Networks 1994; 5(1):96-101.
12. Vose MD, Wright AH. Form invariance and implicit parallelism. Evol Computat 2001; 9(3):355-370.
13. Antonisse HJ. A new interpretation of schema notation that overturns the binary encoding constraint. In: Proc. 3rd Int. Conf. on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann; 1989. 86-91.
14. Goldberg DE. The theory of virtual alphabets. In: Parallel problem solving from nature 1. Lecture Notes in Computer Science, Vol. 496. New York: Springer; 1991. 13-22.
15. Eshelman LJ, Schaffer JD. Real-coded genetic algorithms and interval-schemata. In: Foundations of genetic algorithms 2. San Mateo, CA: Morgan Kaufmann; 1993. 187-202.
16. Radcliffe NJ, Surry PD. Fitness variance of formae and performance prediction. In: Foundations of genetic algorithms 3. San Mateo, CA: Morgan Kaufmann; 1995. 51-72.
17. Michalewicz Z, Schoenauer M. Evolutionary algorithms for constrained parameter optimization problems. Evol Computat 1996; 4(1):1-32.
18. Gen M, Cheng R. A survey of penalty techniques in genetic algorithms. In: Proc. 3rd IEEE Conf. on Evolutionary Computation. Piscataway, NJ: IEEE Press; 1996. 804-809.
19. Miller BL, Goldberg DE. Genetic algorithms, selection schemes, and the varying effects of noise. University of Illinois at Urbana-Champaign; IlliGAL Report, No. 95009. 1995.
20. Macready WG, Wolpert DH. Bandit problems and the exploration/exploitation tradeoff. IEEE Trans Evol Computat 1998; 2(1):2-22.
21. Goldberg DE, Deb K. A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of genetic algorithms. San Mateo, CA: Morgan Kaufmann; 1991. 69-93.
22. Blickle T, Thiele L. A comparison of selection schemes used in genetic algorith. Swiss Federal Institute of Technology; TIK-Report, Nr. 11, 2nd Version. 1995.
23. Bäck T. Selective pressure in evolutionary algorithms: A characterization of selection mechanisms. In: Proc. 1st IEEE Conf. on Evolutionary Computation. Piscataway, NJ: IEEE Press; 1994. 57-62.
24. Whitley LD. The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In: Proc. 3rd Int. Conf. on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann; 1989. 116-121.
25. Syswerda G. Uniform crossover in genetic algorithms. In: Proc. 3rd Int. Conf. on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann; 1989. 2-9.
26. Saravanan N, Fogel DB, Nelson KM. A comparison of methods for self-adaptation in evolutionary algorithms. BioSystems 1995; 36:157-166.
27. Eiben AE. Hinterding R, Michalewicz Z. Parameter control in evolutionary algorithms. IEEE Trans Evol Computat 1999; 3(2):124-141.
28. Rogers A, Prügel-Bennet A. Genetic drift in genetic algorithm selection schemes. IEEE Trans Evol Computat 1999; 3(4):298-303.
29. Sareni B, Krähenbühl L. Fitness sharing and niching methods revisited. IEEE Trans Evol Computat 1998; 2(3):97-106.
30. Cantú-Paz E. Efficient and accurate genetic algorithms. Boston: Kluwer; 2000.
31. Alba E, Troya JM. A survey of parallel distributed genetic algorithms. Complexity 1999; 4(4):31-52.
32. Cantu-Paz E. Markov chain models of parallel genetic algorithms. IEEE Trans Evol Computat 2000; 4(3):216-226.
33. De Jong KA. Genetic algorithms are NOT function optimizers. In: Foundations of genetic algorithms 2. San Mateo, CA: Morgan Kaufmann; 1993. 5-17.
34. Holland JH. Adaptation in natural and artificial systems. University of Michigan Press; Ann Arbor, MI; 1975.
35. Miranda V, Srinivasan D, Proença LM. Evolutionary computation in power systems. Electrical Power & Energy Systems 1998; 20(2):89-98.