Optimization in dynamic environments: A survey on problems, methods and measures

Soft Computing

Volumn 15, Issue 7, 2011, Pages 1427-1448

  Cruz, Carlos ^a   González, Juan R ^a   Pelta, David A ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMIC ENVIRONMENTS; WEB REPOSITORIES;

SURVEYS; USER INTERFACES;

OPTIMIZATION;

EID: 79958044334     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-010-0681-0     Document Type: Article

References (145)

1. Jin Y, Sendhoff B (2004) Constructing dynamic optimization test problems using the multi-objective optimization concept. In: Raidl G. R. (ed) Lecture notes in computer science, vol 3005. Springer, New York, pp 525-536.
2. Karaman A, Uyar S, Eryigit G (2005) The memory indexing evolutionary algorithm for dynamic environments. In: Applications on evolutionary computing. Lecture notes in computer science, vol 3449. Springer, Berlin, pp 563-573.
3. Kobliha M, Schwarz J, Oenáek J (2006) Bayesian optimization algorithms for dynamic problems. In: Applications of evolutionary computing. Lecture notes in computer science, vol 3907. Springer, Berlin, pp 800-804.
4. Arnold DV, Beyer H-G (2006) Optimum tracking with evolution strategies. Evol Comput 14(3): 291-308.
5. Aydin ME, Öztemel E (2000) Dynamic job-shop scheduling using reinforcement learning agents. Robot Auton Syst 33(2-3): 169-178.
6. Kramer G, Gallagher J (2003) Improvements to the *CGA enabling online intrinsic evolution in compact EH devices. In: Proceedings of the NASA/DoD conference on evolvable hardware, pp 225-231.
7. Laredo JL, Castillo PA, Mora AM, Merelo JJ, Rosa A, Fernandes C (2008) Evolvable agents in static and dynamic optimization problems. In: Proceedings of the 10th international conference on parallel problem solving from nature. Springer, New York, pp 488-497.
8. Li C, Yang S (2008a) A generalized approach to construct benchmark problems for dynamic optimization. In: Simulated evolution and learning. Lecture notes in computer science, vol 5361. Springer, Berlin, pp 391-400.
9. Blackwell TM (2005) Particle swarms and population diversity. Soft Comput: A Fusion Found Methodol Appl 9(11): 793-802.
10. Li C, Yang S (2008b) Fast multi-swarm optimization for dynamic optimization problems. In: Fourth international conference on natural computation, vol 7. IEEE Computer Society, pp 624-628.
11. Li X (2004) Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Proceedings of the genetic and evolutionary computation conference. Lecture notes in computer science, vol 3102. Springer, Berlin, pp 105-116.
12. Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evol Comput 10(4): 459-472.
13. Li X, Branke J, Blackwell T. (2006) Particle swarm with speciation and adaptation in a dynamic environment. In: Proceedings of the genetic and evolutionary computation conference, vol 1. ACM, New York, pp 51-58.
14. Lim D, Ong Y-S, Lim M-H, Jin Y (2007) Single/multi-objective inverse robust evolutionary design methodology in the presence of uncertainty. In: Studies in computational intelligence, vol 51. Springer, New York, pp 437-456.
15. Ling Q, Wu G, Wang Q (2007) Deterministic robust optimal design based on standard crowding genetic algorithm. In: Studies in computational intelligence, vol 51. Springer, New York, pp 583-598.
16. Lung RI, Dumitrescu D (2007) A new collaborative evolutionary-swarm optimization technique. In: Proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 2817-2820.
17. Mack Y, Goel T, Shyy W, Haftka R (2007) Surrogate model-based optimization framework: a case study in aerospace design. In: Studies in computational intelligence, vol 51. Springer, New York, pp 323-342.
18. Branke J (2005) Editorial: special issue on dynamic optimization problems. Soft Comput: A Fusion Found Methodol Appl 9(11): 777.
19. Branke J, Jin Y (2006a) Guest editorial special issue on evolutionary computation in the presence of uncertainty. IEEE Trans Evol Comput 10(4): 377-379.
20. Mendes R, Mohais A (2005) DynDE: a differential evolution for dynamic optimization problems. In: Proceedings of the IEEE Congress on evolutionary computation, vol 3, pp 2808-2815.
21. Meyer KD, Nasuto SJ, Bishop M (2006) Stochastic diffusion search: partial function evaluation in swarm intelligence dynamic optimisation. In: Stigmergic optimization. Studies in computational intelligence, vol 31. Springer, Berlin, pp 185-207.
22. Michalewicz Z, Schmidt M, Michalewicz M, Chiriac C (2007) Adaptive business intelligence: three case studies. In: Studies in computational intelligence, vol 51. Springer, New York, pp 179-196.
23. Montemanni R, Gambardella L, Rizzoli A, Donati A (2003) A new algorithm for a dynamic vehicle routing problem based on ant colony system. In: Second international workshop on freight transportation and logistics, pp 27-30.
24. Mori N, Kita H (2000a) Genetic algorithms for adaptation to dynamic environments: a survey. In: IEEE industrial electronics conference, IECON, vol 4, pp 2947-2952.
25. Mori N, Kude T, Matsumoto K (2000b) Adaptation to a dynamical environment by means of the environment identifying genetic algorithm. In: IEEE industrial electronics conference, IECON 2000.
26. Morrison RW (2003) Performance measurement in dynamic environments. In: GECCO Proceedings of workshop on evolutionary algorithms for dynamic optimization problems, pp 5-8.
27. Morrison RW (2004) Designing evolutionary algorithms for dynamic environments. Springer, New York.
28. Morrison R, De Jong K (1999) A test problem generator for non-stationary environments. In: Proceedings of the IEEE Congress on evolutionary computation, vol 3, pp 2047-2053.
29. Moser I, Hendtlass T (2007) A simple and efficient multi-component algorithm for solving dynamic function optimisation problems. In: Proceedings of the IEEE Congress on evolutionary computation, pp 252-259.
30. Neri F, Mäkinen R (2007) Hierarchical evolutionary algorithms and noise compensation via adaptation. In: Studies in computational intelligence, vol 51. Springer, New York, pp 345-369.
31. Du W, Li B (2008) Multi-strategy ensemble particle swarm optimization for dynamic optimization. Inf Sci 178(15): 3096-3109.
32. Novoa P, Pelta DA, Cruz C, del Amo IG (2009) Controlling particle trajectories in a multi-swarm approach for dynamic optimization problems. In: International work-conference on the interplay between natural and artificial computation, IWINAC 2009. Lecture notes in computer science, vol 5601. Springer, Berlin, pp 285-294.
33. Olivetti de França F, Von Zuben FJ, Nunes de Castro L (2005) An artificial immune network for multimodal function optimization on dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 289-296.
34. Parrott D, Li X (2004) A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Proceedings of the IEEE Congress on evolutionary computation, vol 1, pp 98-103.
35. Peng B, Reynolds R (2004) Cultural algorithms: knowledge learning in dynamic environments. In: Proceedings of the IEEE Congress on evolutionary computation, pp 1751-1758.
36. Plexousakis D (2006) Beyond the horizon: anticipating future and emerging information society technologies. Technical report, European Research Consortium for Informatics and Mathematics. http://beyond-the-horizon. ics. forth. gr/.
37. Esquivel SC, Coello Coello CA (2006) Hybrid particle swarm optimizer for a class of dynamic fitness landscape. Eng Optim 38: 873-888.
38. Quintão F, Nakamura F, Mateus G (2007) Evolutionary algorithms for combinatorial problems in the uncertain environment of the wireless sensor networks. In: Studies in computational intelligence, vol 51. Springer, New York, pp 197-222.
39. Rand W, Riolo R (2005) Shaky ladders, hyperplane-defined functions and genetic algorithms: systematic controlled observation in dynamic environments. In: Applications on evolutionary computing. Lecture notes in computer science, vol 3449. Springer, Berlin, pp 600-609.
40. Rand W, Riolo R (2006) The effect of building block construction on the behavior of the GA in dynamic environments: a case study using the shaky ladder hyperplane-defined functions. In: Applications of evolutionary computing. Lecture notes in computer science, vol 3907. Springer Berlin, pp 776-787.
41. Reyes-Sierra M, Coello C (2007) A study of techniques to improve the efficiency of a multi-objective particle swarm optimizer. In: Studies in computational intelligence, vol 51. Springer, New York, pp 269-296.
42. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 special session on real parameter optimization. J Heuristics 15: 617-644.
43. Ghosh A, Mühlenbein H (2004) Univariate marginal distribution algorithms for non-stationary optimization problems. Int J Knowl Intell Eng Syst 8: 129-138.
44. Richter H (2005) A study of dynamic severity in chaotic fitness landscapes. In: Proceedings of the IEEE Congress on evolutionary computation, vol 3, pp 2824-2831.
45. Rocco C, Salazar D (2007) A hybrid approach based on evolutionary strategies and interval arithmetic to perform robust designs. In: Studies in computational intelligence, vol 51. Springer, New York, pp 543-564.
46. Rohlfshagen P, Yao X (2009) The dynamic knapsack problem revisited: a new benchmark problem for dynamic combinatorial optimisation. In: Applications of evolutionary computing, pp 745-754.
47. Rohlfshagen P, Lehre PK, Yao X (2009) Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change. In: Proceedings of the genetic and evolutionary computation conference, pp 1713-1720.
48. Ronnewinkel C, Martinetz T (2001) Explicit speciation with few a priori parameters for dynamic optimization problems. In: GECCO workshop on evolutionary algorithms for dynamic optimization problems. Morgan Kaufmann, Massachusetts, pp 31-34.
49. Saleem S, Reynolds R (2000) Cultural algorithms in dynamic environments. In Proceedings of the Congress on evolutionary computation, vol 2, pp 1513-1520.
50. Schönemann L (2004) The impact of population sizes and diversity on the adaptability of evolution strategies in dynamic environments. In: Proceedings of the IEEE Congress on evolutionary computation, vol 2, pp 1270-1277.
51. Hanshar FT, Ombuki-Berman BM (2007) Dynamic vehicle routing using genetic algorithms. Appl Intell 27(1): 89-99.
52. Schönemann L (2007) Evolution strategies in dynamic environments. In: Studies in computational intelligence, vol 51. Springer, New York, pp 51-77.
53. Hooker JN (1995) Testing heuristics: we have it all wrong. J Heuristics 1(1): 33-42.
54. Sheskin DJ (2004) Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca Raton.
55. Shi Y, Eberhart R (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the IEEE conference on evolutionary computation.
56. Simões A, Costa E (2003) An immune system-based genetic algorithm to deal with dynamic environments: diversity and memory. In: Pearson DW, Steele NC, Albrecht R (eds) Proceedings of the sixth international conference on neural networks and genetic algorithms (ICANNGA03). Springer, New York, pp 168-174.
57. Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments: a survey. IEEE Trans Evol Comput 9(3): 303-317.
58. Stanhope S, Daida J (1999) (1+1) Genetic algorithm fitness dynamics in a changing environment. In Proceedings of the IEEE Congress on evolutionary computation, vol 3, pp 1851-1858.
59. Tenne Y, Armfield S (2007) A memetic algorithm using a trust-region derivative-free optimization with quadratic modelling for optimization of expensive and noisy black-box functions. In: Studies in computational intelligence, vol 51. Springer, New York, pp 389-415.
60. Tezuka M, Munetomo M, Akama K (2007) Genetic algorithm to optimize fitness function with sampling error and its application to financial optimization problem. In: Studies in computational intelligence, vol 51. Springer, New York, pp 417-434.
61. Tinós R, Yang S (2007a) Genetic algorithms with self-organizing behaviour in dynamic environments. In: Studies in computational intelligence, vol 51. Springer, New York, pp 105-127.
62. Tinós R, Yang S (2007c) Continuous dynamic problem generators for evolutionary algorithms. Proceedings of the IEEE Congress on evolutionary computation, pp 236-243.
63. Tinós R, Yang S (2008) Evolutionary programming with q-Gaussian mutation for dynamic optimization problems. In: Proceedings of the IEEE Congress on evolutionary computation, pp 1823-1830.
64. Tumer K, Agogino A (2007) Evolving multi rover systems in dynamic and noisy environments. In: Studies in computational intelligence, vol 51. Springer, New York, pp 371-387.
65. Ursem RK (2000) Multinational GAs: multimodal optimization techniques in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, Massachusetts, pp 19-26.
66. Venayagamoorthy G (2004) Adaptive critics for dynamic particle swarm optimization. In: IEEE international symposium on intelligent control.
67. Weicker K (2002) Performance measures for dynamic environments. In: Parallel problem solving from nature VII. Lecture notes in computer science, vol 2439. Springer, New York, pp 64-73.
68. Weicker K (2003) Evolutionary algorithms and dynamic optimization problems. Der Andere Verlag.
69. Weicker K, Weicker N (1999) On evolution strategy optimization in dynamic environments. In: Proceedings of the IEEE Congress on evolutionary computation, pp 2039-2046.
70. Lung RI, Dumitrescu D (2009) Evolutionary swarm cooperative optimization in dynamic environments. Nat Comput 9(1): 83-94.
71. Wineberg M, Oppacher F (2000) Enhancing the GA's ability to cope with dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, Massachusetts, pp 3-10.
72. Mattfeld DC, Bierwirth C (2004) An efficient genetic algorithm for job shop scheduling with tardiness objectives. Eur J Oper Res 155(3): 616-630.
73. Yan X-S, Kang L-S, Cai Z-H, Li H (2004) An approach to dynamic traveling salesman problem. In: International conference on machine learning and cybernetics.
74. Yang S (2003) Non-stationary problem optimization using the primal-dual genetic algorithm. In: Proceedings of the IEEE Congress on evolutionary computation, vol 3. IEEE Press, pp 2246-2253.
75. Yang S (2005) Memory-based immigrants for genetic algorithms in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 1115-1122.
76. Yang S (2006a) Associative memory scheme for genetic algorithms in dynamic environments. In: Applications of evolutionary computing. Lecture notes in computer science, vol 3907. Springer, Berlin, pp 788-799.
77. Yang S (2006b) A comparative study of immune system based genetic algorithms in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 1377-1384.
78. Yang S (2007) Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Studies in computational intelligence, vol 51. Springer, New York, pp 3-28.
79. Yang S, Ong Y-S, Jin Y (eds) (2007) Evolutionary computation in dynamic and uncertain environments. In: Studies in computational intelligence, vol 51. Springer, Berlin.
80. Yen G, Yang F, Hickey T, Goldstein M (2001) Coordination of exploration and exploitation in a dynamic environment. In: International joint conference on neural networks. Institute of Electrical and Electronics Engineers.
81. Morrison R, De Jong K (1999) A test problem generator for non-stationary environments. In: Proceedings of the IEEE Congress on evolutionary computation, vol 3, pp 2047-2053.
82. Moser I, Hendtlass T (2007) A simple and efficient multi-component algorithm for solving dynamic function optimisation problems. In: Proceedings of the IEEE Congress on evolutionary computation, pp 252-259.
83. Neri F, Mäkinen R (2007) Hierarchical evolutionary algorithms and noise compensation via adaptation. In: Studies in computational intelligence, vol 51. Springer, New York, pp 345-369.
84. Novoa P, Pelta DA, Cruz C, del Amo IG (2009) Controlling particle trajectories in a multi-swarm approach for dynamic optimization problems. In: International work-conference on the interplay between natural and artificial computation, IWINAC 2009. Lecture notes in computer science, vol 5601. Springer, Berlin, pp 285-294.
85. Olivetti de França F, Von Zuben FJ, Nunes de Castro L (2005) An artificial immune network for multimodal function optimization on dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 289-296.
86. Pankratz G (2005) Dynamic vehicle routing by means of a genetic algorithm. Int J Phys Distrib Logist Manag 35(5): 362-383.
87. Parrott D, Li X (2004) A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Proceedings of the IEEE Congress on evolutionary computation, vol 1, pp 98-103.
88. Pelta D, Cruz C, Gonzalez JR (2009a) A study on diversity and cooperation in a multiagent strategy for dynamic optimization problems. Int J Intell Syst 24: 844-861.
89. Pelta D, Cruz C, Verdegay JL (2009b) Simple control rules in a cooperative system for dynamic optimisation problems. Int J Gen Syst 38(7): 701-717.
90. Peng B, Reynolds R (2004) Cultural algorithms: knowledge learning in dynamic environments. In: Proceedings of the IEEE Congress on evolutionary computation, pp 1751-1758.
91. Plexousakis D (2006) Beyond the horizon: anticipating future and emerging information society technologies. Technical report, European Research Consortium for Informatics and Mathematics. http://beyond-the-horizon. ics. forth. gr/.
92. Quintão F, Nakamura F, Mateus G (2007) Evolutionary algorithms for combinatorial problems in the uncertain environment of the wireless sensor networks. In: Studies in computational intelligence, vol 51. Springer, New York, pp 197-222.
93. Rand W, Riolo R (2005) Shaky ladders, hyperplane-defined functions and genetic algorithms: systematic controlled observation in dynamic environments. In: Applications on evolutionary computing. Lecture notes in computer science, vol 3449. Springer, Berlin, pp 600-609.
94. Rand W, Riolo R (2006) The effect of building block construction on the behavior of the GA in dynamic environments: a case study using the shaky ladder hyperplane-defined functions. In: Applications of evolutionary computing. Lecture notes in computer science, vol 3907. Springer Berlin, pp 776-787.
95. Rardin RL, Uzsoy R (2001) Experimental evaluation of heuristic optimization algorithms: a tutorial. J Heuristics 7(3): 261-304.
96. Reyes-Sierra M, Coello C (2007) A study of techniques to improve the efficiency of a multi-objective particle swarm optimizer. In: Studies in computational intelligence, vol 51. Springer, New York, pp 269-296.
97. Richter H (2005) A study of dynamic severity in chaotic fitness landscapes. In: Proceedings of the IEEE Congress on evolutionary computation, vol 3, pp 2824-2831.
98. Richter H, Yang S (2009) Learning behavior in abstract memory schemes for dynamic optimization problems. Soft Comput: A Fusion Found Methodol Appl 13(12): 1163-1173.
99. Rocco C, Salazar D (2007) A hybrid approach based on evolutionary strategies and interval arithmetic to perform robust designs. In: Studies in computational intelligence, vol 51. Springer, New York, pp 543-564.
100. Rohlfshagen P, Yao X (2009) The dynamic knapsack problem revisited: a new benchmark problem for dynamic combinatorial optimisation. In: Applications of evolutionary computing, pp 745-754.
101. Rohlfshagen P, Lehre PK, Yao X (2009) Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change. In: Proceedings of the genetic and evolutionary computation conference, pp 1713-1720.
102. Ronnewinkel C, Martinetz T (2001) Explicit speciation with few a priori parameters for dynamic optimization problems. In: GECCO workshop on evolutionary algorithms for dynamic optimization problems. Morgan Kaufmann, Massachusetts, pp 31-34.
103. Rossi C, Abderrahim M, César Díaz J (2008) Tracking moving optima using Kalman-based predictions. Evol Comput 16(1): 1-30.
104. Saleem S, Reynolds R (2000) Cultural algorithms in dynamic environments. In Proceedings of the Congress on evolutionary computation, vol 2, pp 1513-1520.
105. Schönemann L (2004) The impact of population sizes and diversity on the adaptability of evolution strategies in dynamic environments. In: Proceedings of the IEEE Congress on evolutionary computation, vol 2, pp 1270-1277.
106. Schönemann L (2007) Evolution strategies in dynamic environments. In: Studies in computational intelligence, vol 51. Springer, New York, pp 51-77.
107. Sheskin DJ (2004) Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca Raton.
108. Shi Y, Eberhart R (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the IEEE conference on evolutionary computation.
109. Simões A, Costa E (2003) An immune system-based genetic algorithm to deal with dynamic environments: diversity and memory. In: Pearson DW, Steele NC, Albrecht R (eds) Proceedings of the sixth international conference on neural networks and genetic algorithms (ICANNGA03). Springer, New York, pp 168-174.
110. Smierzchalski R, Michalewicz Z (2000) Modeling of ship trajectory in collision situations by an evolutionary algorithms. IEEE Trans Evol Comput 4: 227-241.
111. Stanhope S, Daida J (1999) (1+1) Genetic algorithm fitness dynamics in a changing environment. In Proceedings of the IEEE Congress on evolutionary computation, vol 3, pp 1851-1858.
112. Tenne Y, Armfield S (2007) A memetic algorithm using a trust-region derivative-free optimization with quadratic modelling for optimization of expensive and noisy black-box functions. In: Studies in computational intelligence, vol 51. Springer, New York, pp 389-415.
113. Tezuka M, Munetomo M, Akama K (2007) Genetic algorithm to optimize fitness function with sampling error and its application to financial optimization problem. In: Studies in computational intelligence, vol 51. Springer, New York, pp 417-434.
114. Tinós R, Yang S (2007a) Genetic algorithms with self-organizing behaviour in dynamic environments. In: Studies in computational intelligence, vol 51. Springer, New York, pp 105-127.
115. Tinós R, Yang S (2007b) A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genet Program Evolvable Mach 8(3): 255-286.
116. Tinós R, Yang S (2007c) Continuous dynamic problem generators for evolutionary algorithms. Proceedings of the IEEE Congress on evolutionary computation, pp 236-243.
117. Tinós R, Yang S (2008) Evolutionary programming with q-Gaussian mutation for dynamic optimization problems. In: Proceedings of the IEEE Congress on evolutionary computation, pp 1823-1830.
118. Trojanowski K, Wierzchon ST (2009) Immune-based algorithms for dynamic optimization. Inf Sci 179(10): 1495-1515.
119. Tumer K, Agogino A (2007) Evolving multi rover systems in dynamic and noisy environments. In: Studies in computational intelligence, vol 51. Springer, New York, pp 371-387.
120. Ursem RK (2000) Multinational GAs: multimodal optimization techniques in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, Massachusetts, pp 19-26.
121. Ursem RK, Krink T, Jensen M, Michalewicz Z (2002) Analysis and modeling of control tasks in dynamic systems. IEEE Trans Evol Comput 6(4): 378-389.
122. Venayagamoorthy G (2004) Adaptive critics for dynamic particle swarm optimization. In: IEEE international symposium on intelligent control.
123. Wang H, Wang D, Yang S (2009a) A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput: A Fusion Found Methodol Appl 13(8-9): 763-780.
124. Wang H, Yang S, Ip W, Wang D (2009b) Adaptive primal-dual genetic algorithms in dynamic environments. IEEE Trans Syst Man Cybernet B 39(6): 1348-1361.
125. Weicker K (2002) Performance measures for dynamic environments. In: Parallel problem solving from nature VII. Lecture notes in computer science, vol 2439. Springer, New York, pp 64-73.
126. Weicker K (2003) Evolutionary algorithms and dynamic optimization problems. Der Andere Verlag.
127. Weicker K, Weicker N (1999) On evolution strategy optimization in dynamic environments. In: Proceedings of the IEEE Congress on evolutionary computation, pp 2039-2046.
128. Wineberg M, Oppacher F (2000) Enhancing the GA's ability to cope with dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, Massachusetts, pp 3-10.
129. Woldesenbet YG, Yen GG (2009) Dynamic evolutionary algorithm with variable relocation. IEEE Trans Evol Comput 13(3): 500-513.
130. Yan X-S, Kang L-S, Cai Z-H, Li H (2004) An approach to dynamic traveling salesman problem. In: International conference on machine learning and cybernetics.
131. Yang S (2003) Non-stationary problem optimization using the primal-dual genetic algorithm. In: Proceedings of the IEEE Congress on evolutionary computation, vol 3. IEEE Press, pp 2246-2253.
132. Yang S (2005) Memory-based immigrants for genetic algorithms in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 1115-1122.
133. Yang S (2006a) Associative memory scheme for genetic algorithms in dynamic environments. In: Applications of evolutionary computing. Lecture notes in computer science, vol 3907. Springer, Berlin, pp 788-799.
134. Yang S (2006b) A comparative study of immune system based genetic algorithms in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference. ACM, New York, pp 1377-1384.
135. Yang S (2007) Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Studies in computational intelligence, vol 51. Springer, New York, pp 3-28.
136. Yang S (2008) Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evol Comput 16(3): 385-416.
137. Yang S, Tinós R (2007) A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Autom Comput 4(3): 243-254.
138. Yang S, Yao X (2005) Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput: A Fusion Found Methodol Appl 9(11): 815-834.
139. Yang S, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput 12(5): 542-561.
140. Yang S, Ong Y-S, Jin Y (2006) Editorial to special issue on evolutionary computation in dynamic and uncertain environments. Genet Program Evolvable Mach 7(4): 293-294.
141. Yang S, Ong Y-S, Jin Y (eds) (2007) Evolutionary computation in dynamic and uncertain environments. In: Studies in computational intelligence, vol 51. Springer, Berlin.
142. Yang S, Cheng H, Wang F (2010) Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Trans Syst Man Cybernet C: Appl Rev 40(99): 52-63.
143. Yen G, Yang F, Hickey T, Goldstein M (2001) Coordination of exploration and exploitation in a dynamic environment. In: International joint conference on neural networks. Institute of Electrical and Electronics Engineers.
144. Zeng S, Shi H, Kang L, Ding L (2007) Orthogonal dynamic hill climbing algorithm: ODHC. In: Studies in computational intelligence, vol 51. Springer, New York, pp 79-104.
145. Zou X, Wang M, Zhou A, Mckay B (2004) Evolutionary optimization based on chaotic sequence in dynamic environments. In: IEEE international conference on networking, sensing and control, pp 1364-1369.