Definition of the Subject
Evolutionary algorithms (EAs) are stochastic population-based methods inspired by nature. A population consists of several individuals usually encoded as vectors. During an evolutionary process, a population is transformed into a new population. After some such transformations, the algorithm stops and returns a best found individual as solution.
Differential Evolution (DE) is an evolutionary algorithm for global optimization over continuous spaces as well as for optimization over discrete spaces. Nowadays, it is used as a powerful global optimization method within a wide range of research areas.
As a plethora of data are generated in every possible means and data dimensionality increases on a large scale, it is imperative to increase power of methods in data mining, knowledge discovery, as well as in optimization methods that are dealing with high-dimensional massive data, uncertainty environments, and dynamic systems.
Introduction
(Das and Suganthan 2011): To...
Abbreviations
- Control parameter:
-
Control parameter determines behavior of evolutionary program (e.g., crossover rate, population size, etc.).
- Differential evolution:
-
An evolutionary algorithm for global optimization, which realized the evolution of a population of individuals using mutation, crossover, and selection operation.
- Evolutionary computation:
-
Solution approach guided by biological evolution, which begins with potential solution models, then iteratively applies an algorithm to find the fittest models from the set to serve as inputs to the next generation, ultimately leading to a model that best represents the data.
- Individual:
-
An individual represents a candidate solution. During the optimization process an evolutionary algorithm usually uses a population of individuals to solve a particular problem.
- Search space:
-
Set of all possible situations of the optimization problem that we want to solve.
- Self-adaptation:
-
The ability that allows an evolutionary algorithm to adapt itself to any general class of problems, by reconfiguring itself accordingly, and do this without user’s interaction. Self-adaptation is applied on control parameter(s).
Bibliography
Bäck T, Fogel DB, Michalewicz Z (eds) (1997) Handbook of evolutionary computation, IOP Publishing Ltd., Bristol, UK
Bošković B, Brest J, Zamuda A, Greiner S, Žumer V (2011) History mechanism supported differential evolution for chess evaluation function tuning. Soft Comput Fusion Found Methodol Appl 15:667–682
Brest J, Maučec MS (2008) Population size reduction for the differential evolution algorithm. Appl Intell 29(3):228–247
Brest J, Greiner S, Bošković B, Mernik M, Žumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657
Brest J, Korošec P, Šilc J, Zamuda A, Bošković B, Maučec MS (2013) Differential evolution and differential ant-stigmergy on dynamic optimisation problems. Int J Syst Sci 44:663–679
Brest J, Zamuda A, Bošković B (2015) Adaptation in the differential evolution. In: Fister I, Fister I Jr (eds) Adaptation and hybridization in computational intelligence. Adaptation, learning, and optimization, vol 18. Springer International Publishing, Cham, CH. pp 53–68
Cheng J, Zhang G, Neri F (2013) Enhancing distributed differential evolution with multicultural migration for global numerical optimization. Inform Sci 247:72–93
Das S, Suganthan P (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):27–54
Das S, Abraham A, Chakraborty U, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553
Eiben AE, Smith JE (2003) Introduction to evolutionary computing, Natural computing. Springer, Berlin
Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141
Elsayed S, Sarker R, Essam D (2014) A self-adaptive combined strategies algorithm for constrained optimization using differential evolution. Appl Math Comput 241:267–282
Feoktistov V (2006) Differential evolution: in search of solutions (Springer optimization and its applications). Springer, New York/Secaucus
Glotić A, Zamuda A (2015) Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution. Appl Energy 141:42–56
Karafotias G, Hoogendoorn M, Eiben A (2015) Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans Evol Comput 19(2):167–187
Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462
Mallipeddi R, Suganthan P (2010) Ensemble of constraint handling techniques. IEEE Trans Evol Comput 14(4):561–579
Mukhopadhyay A, Maulik U, Bandyopadhyay S, Coello C (2014a) Survey of multiobjective evolutionary algorithms for data mining: part II. IEEE Trans Evol Comput 18(1):20–35
Mukhopadhyay A, Maulik U, Bandyopadhyay S, Coello Coello C (2014b) A survey of multiobjective evolutionary algorithms for data mining: part I. IEEE Trans Evol Comput 18(1):4–19
Neri F, Tirronen V (2009) Scale factor local search in differential evolution. Memetic Comp 1(2):153–171
Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33(1–2):61–106
Price KV, Storn RM, Lampinen JA (2005) Differential evolution, a practical approach to global optimization. Springer, Berlin Heidelberg, Germany.
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417
Storn R, Price K (1995) Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report TR-95-012, Berkeley
Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359
Tanabe R, Fukunaga A (2013) Evaluating the performance of shade on cec 2013 benchmark problems. In: 2013 IEEE congress on evolutionary computation (CEC), 20–23 june, 2013, Cancun, Mexico. IEEE, pp 1952–1959
Tanabe R, Fukunaga A (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC2014), 6–11 July 2014, Beijing, China. IEEE, pp 1658–1665
Teng NS, Teo J, Hijazi MHA (2009) Self-adaptive population sizing for a tune-free differential evolution. Soft Comput Fusion Found Methodol Appl 13(7):709–724
Wang H, Rahnamayan S, Wu Z (2013) Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems. J Parallel Distrib Comput 73(1):62–73
Wu X, Zhu X, Wu G-Q, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107
Zamuda A, Brest J (2014) Vectorized procedural models for animated trees reconstruction using differential evolution. Inform Sci 278:1–21
Zamuda A, Sosa JDH (2014) Differential evolution and underwater glider path planning applied to the short-term opportunistic sampling of dynamic mesoscale ocean structures. Appl Soft Comput 24:95–108
Zamuda A, Brest J, Bošković B, Žumer V (2011) Differential evolution for parameterized procedural woody plant models reconstruction. Appl Soft Comput 11:4904–4912
Zhang J, Sanderson A (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
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Brest, J. (2015). Advanced Evolutionary Algorithms in Data Mining. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_650-1
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DOI: https://doi.org/10.1007/978-3-642-27737-5_650-1
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