Genetic Algorithms in Applications

[1]  N. Jimenez-Redondo,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms [discussion and closure] , 2001 .

[2]  H. Chen,et al.  Cooperative Coevolutionary Algorithm for Unit Commitment , 2002, IEEE Power Engineering Review.

[3]  S. M. Shahidehpour,et al.  Short-term unit commitment expert system , 1990 .

[4]  A. Conejo,et al.  A parallel repair genetic algorithm to solve the unit commitment problem , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[5]  A. H. Mantawy,et al.  Unit commitment by tabu search , 1998 .

[6]  Hiroshi Sasaki,et al.  A solution method of unit commitment by artificial neural networks , 1992 .

[7]  A. H. Mantawy,et al.  A genetic-based algorithm for fuzzy unit commitment model , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[8]  S. O. Orero,et al.  Scheduling of generators with a hybrid genetic algorithm , 1995 .

[9]  Chuan-Ping Cheng,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms , 2000 .

[10]  S. M. Shahidehpour,et al.  An intelligent dynamic programming for unit commitment application , 1991 .

[11]  A. H. Mantawy,et al.  A new genetic-based tabu search algorithm for unit commitment problem , 1999 .

[12]  Hans P. Van Meeteren Scheduling of Generation and Allocation of Fuel, Using Dynamic and Linear Programming , 1984 .

[13]  Shokri Z. Selim,et al.  A new genetic algorithm approach for unit commitment , 1997 .

[14]  S. M. Shahidehpour,et al.  A hybrid artificial neural network-dynamic programming approach to unit commitment , 1992 .

[15]  Gerald B. Sheblé,et al.  A profit-based unit commitment GA for the competitive environment , 2000 .

[16]  Hong-Tzer Yang,et al.  Optimization of unit commitment using parallel structures of genetic algorithm , 1995, Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95.

[17]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[18]  Me El-Hawary Applications of artificial neural networks in electric power systems operational planning , 1999 .

[19]  Tomonobu Senjyu,et al.  A unit commitment problem by using genetic algorithm based on unit characteristic classification , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[20]  Mark O'Malley,et al.  Augmented Hopfield network for unit commitment and economic dispatch , 1997 .

[21]  P. G. Lowery,et al.  Generating Unit Commitment by Dynamic Programming , 1966 .

[22]  Gerald B. Sheblé,et al.  Unit commitment literature synopsis , 1994 .

[23]  P. Ronne-Hansen,et al.  Neural networks as a tool for unit commitment , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[24]  C. Lucas,et al.  A new genetic algorithm with Lamarckian individual learning for generation scheduling , 2003 .

[25]  Robert E. Smith,et al.  A genetic algorithm based approach to thermal unit commitment of electric power systems , 1995 .

[26]  A. Feijóo,et al.  Synchronization of Asynchronous Wind Turbines , 2002, IEEE Power Engineering Review.

[27]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[28]  Jizhong Zhu,et al.  Optimal generation scheduling based on AHP/ANP , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[29]  Hong-Tzer Yang,et al.  Applications of the genetic algorithm to the unit commitment problem in power generation industry , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[30]  John J. Grefenstette,et al.  How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.

[31]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[32]  M. A. Abido,et al.  A simulated annealing algorithm for fuzzy unit commitment problem , 1999, 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333).

[33]  Dipankar Dasgupta,et al.  Short term unit-commitment using genetic algorithms , 1993, Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93).

[34]  A.H. Mantawy,et al.  A new simulated annealing-based tabu search algorithm for unit commitment , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[35]  D. P. Kothari,et al.  Modern power system analysis / D.P. Kothari, I.J. Nagrath , 2003 .

[36]  Walter L. Snyder,et al.  Dynamic Programming Approach to Unit Commitment , 1987, IEEE Transactions on Power Systems.

[37]  Hong-Tzer Yang,et al.  A parallel genetic algorithm approach to solving the unit commitment problem: implementation on the transputer networks , 1997 .

[38]  A. H. Mantawy,et al.  A simulated annealing algorithm for unit commitment , 1998 .

[39]  K. P. Wong,et al.  Artificial intelligence algorithm for daily scheduling of thermal generators , 1991 .

[40]  C. A. Dortolina,et al.  An approach to solve the unit commitment problem using genetic algorithm , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[41]  Gerald B. Sheblé,et al.  Unit commitment by genetic algorithm with penalty methods and a comparison of Lagrangian search and genetic algorithm—economic dispatch example , 1996 .

[42]  F. N. Lee,et al.  Short-term thermal unit commitment-a new method , 1988 .

[43]  Hiroyuki Mori,et al.  Unit commitment using Tabu search with restricted neighborhood , 1996, Proceedings of International Conference on Intelligent System Application to Power Systems.

[44]  Jonathan F. Bard,et al.  Short-Term Scheduling of Thermal-Electric Generators Using Lagrangian Relaxation , 1988, Oper. Res..

[45]  Kit Po Wong,et al.  Artificial intelligence-based machine-learning system for thermal generator scheduling , 1995 .

[46]  Zhao Hongwei,et al.  A new genetic algorithm for unit commitment , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[47]  F. Galiana,et al.  Demand-side reserve offers in joint energy/reserve electricity markets , 2003 .

[48]  Eiichi Tanaka,et al.  An Evolutionary Programming Solution to the Unit Commitment Problem , 1997 .

[49]  Gwo-Ching Liao,et al.  The use of genetic algorithm/fuzzy system and tabu search for short-term unit commitment , 2002, Proceedings. International Conference on Power System Technology.

[50]  Shokri Z. Selim,et al.  Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem , 1999 .

[51]  Yuan-Yih Hsu,et al.  A hybrid artificial neural network-differential dynamic programming approach for short-term hydro scheduling , 1995 .

[52]  Saifur Rahman,et al.  Network programming technique for unit commitment , 1995 .

[53]  Hong-Tzer Yang,et al.  Solving the unit commitment problem with a genetic algorithm through a constraint satisfaction technique , 1996 .

[54]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[55]  Malcolm Irving,et al.  A genetic algorithm for generator scheduling in power systems , 1996 .

[56]  S. M. Shahidehpour,et al.  Unit commitment with transmission security and voltage constraints , 1999 .

[57]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[58]  Francisco D. Galiana,et al.  Unit commitment by simulated annealing , 1990 .