Abstract
Memetic algorithms (MAs) represent an emerging field that has attracted increasing research interest in recent times. Despite the popularity of the field, we remain to know rather little of the search mechanisms of MAs. Given the limited progress made on revealing the intrinsic properties of some commonly used complex benchmark problems and working mechanisms of Lamarckian memetic algorithms in general non-linear programming, we introduce in this work for the first time the concepts of local optimum structure and generalize the notion of neighborhood to connectivity structure for analysis of MAs. Based on the two proposed concepts, we analyze the solution quality and computational efficiency of the core search operators in Lamarckian memetic algorithms. Subsequently, the structure of local optimums of a few representative and complex benchmark problems is studied to reveal the effects of individual learning on fitness landscape and to gain clues into the success or failure of MAs. The connectivity structure of local optimum for different memes or individual learning procedures in Lamarckian MAs on the benchmark problems is also investigated to understand the effects of choice of memes in MA design.
Similar content being viewed by others
Abbreviations
- f (x):
-
Objective or fitness function
- x*:
-
Global optimum
- x (i) :
-
ith element of vector x
- r (t)(y|x):
-
Conditional probability density function of having offspring y given parent x at generation t
- d(x, y):
-
Euclidean distance \({\|{\bf x}-{\bf y}\|=\sqrt{\sum_{i=1}^{n}{(x_i - y_i)^2}}}\) between x and y
- Ψ :
-
A set of local optimums
- B v :
-
Basin of attraction of local optimum v
- p v (x):
-
Probability of converging to local optimum v from x by means of individual learning
- T (x, y):
-
Probability of converging to local optimum y from x by means of reproduction and individual learning
- C(x′, x′′):
-
Computational effort incurred to arrive at x′′ from x′ by means of individual learning
- E[.|P]:
-
Expectation of a measure conditioned to population P
- C v :
-
Maximum computational effort required to converge to local optimum starting from any point within the basin of attraction B v
- n :
-
Number of dimensions
- N :
-
Population size
- S(.):
-
Selection operator
- R(.):
-
Reproduction operator
- I L(.):
-
Individual learning operator
References
Ong YS, Lim MH, Neri F, Ishibuchi H (2008) Special issue on emerging trends in soft computing: memetic algorithms. Soft Comput Fusion Found Methodol Appl 13(8-9): 739–740
Ong YS, Krasnogor N, Ishibuchi H (2007) Special issue on memetic algorithms. IEEE Trans Syst Man Cybern Part B 37(1): 2–5
Neri F, Moscato P, Ishibuchi H (2009) Special session: memetic algorithms for hard to solve problems. IEEE World Congr Comput Intell
Ong YS, Neri F, Ishibuchi H, Lim MH (2007, 2008) Memetic algorithms: special session. IEEE World Congr Comput Intell
Lim MH, Gustafson S, Krasnogor N, Ong YS (2009) Editorial to the first issue. Memetic Comput 1(1): 1–2
Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program, C3P Report 826
Gwee BH, Lim MH (1996) Polynominoes tiling by a genetic algorithm. Comput Optim Appl J 6(3): 273–291
Lim MH, Yu Y, Omatu S (2000) Efficient genetic algorithms using simple genes exchange local search policy for the quadratic assignment problem. Comput Optim Appl 15(3): 249–268
Lewis R, Paechter B (2007) Finding feasible timetables using group-based operators. IEEE Trans Evol Comput 11(3): 397–413
Ong YS, Keane A (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2): 99–110
Vicini A, Quagliarella D (1999) Airfoil and wing design through hybrid optimization strategies. Am Inst Aeronaut Astronaut J 37(5): 634–641
Michalewicz Z (1996) Genetic Algorithms—Data Structures—Evolution Programs. Springer, London
Houck C, Joines J, Kay M (1996) Utilizing Lamarckian evolution and the Baldwin effect in hybrid genetic algorithms. Tech Rep
Hart WE (1994) Adaptive global optimization with local search. Ph.D. dissertation, University of California, San Diego
Renders J, Bersini H (1994) Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways. IEEE World Congress Comput Intell 1: 312–317
Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold Company, New York
Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12(1): 107–125
Ong YS, Nair PB, Lum K (2006) Max-min surrogate-assisted evolutionary algorithm for robust design. IEEE Trans Evol Comput 10(4): 392–404
Krasnogor N, Smith JE (2005) A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput 9(5): 474–488
Ong YS, Nair PB, Keane AJ (2003) Evolutionary optimization of computationally expensive problems via surrogate modeling. Am Inst Aeronaut Astronaut J 41(4): 687–696
Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans Evol Comput 7(2): 204–223
Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. Ph.D. dissertation, Doctoral dissertation, University of the West of England, Bristol, England
Krasnogor N, Blackburne BP, Burke EK, Hirst JD (2002) Multimeme algorithms for protein structure prediction. In: Proceedings of the parallel problem solving from nature VII (Lecture notes in computer science), vol 2439/2002, pp 769–778
Meuth R, Lim MH, Ong YS, Wunsch DC II (2009) A proposition on memes and meta-memes in computing for higher-order learning. Memetic Comput 1(2): 85–100
Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern Part B 36(1): 141–152
Hinton GE, Nowlan SJ (1987) How learning can guide evolution. Complex Syst 1(1): 495–502
Borenstein E, Meilijson I, Ruppin E (2006) The effect of phenotypic plasticity on evolution in multipeaked fitness landscapes. J Evol Biol 19(5): 1555–1570
Paenke I, Kawecki T, Sendhoff B (2009) The influence of learning on evolution: a mathematical framework. Artif Life 15(2): 227–245
Paenke I, Jin Y, Branke J (2009) Balancing population and individual level adaptation in changing environments. Adapt Behav 17(2): 153–174
Merz P (2004) Advanced fitness landscape analysis and the performance of memetic algorithms. Evol Comput 12(3): 303–325
Merz P (2000) Memetic algorithms for combinatorial optimization problems: fitness landscapes and effective search strategies. Ph.D. dissertation, University of Siegen, Germany
Whitley D, Gordon V, Mathias K (1994) Lamarckian evolution, the Baldwin effect and function optimization. In: Parallel problem solving from nature–PPSN III: international conference on evolutionary computation, the third conference on parallel problem solving from nature, pp 6–15
Paenke I, Sendhoff B, Rowe J, Fernando C (2007) On the adaptive disadvantage of Lamarckianism in rapidly changing environments. In: European conference on artificial life (ECAL), 10–14 September 2007 (Lecture notes in computer science), vol 4648, pp 355–364
Zhang JQ, Sanderson AC (2009) Adaptive differential evolution: a robust approach to multimodal problem optimization. Series on adaptation, Learning and Optimization, vol. 1
Jablonka E, Lamb MJ (1995) Epigenetic inheritance and evolution: the Lamarckian dimension. Oxford University Press, Oxford
Ho MW (1996) Why Lamarck won’t go away. Ann Hum Genet 60(1): 81–84
Beyer H-G (1997) An alternative explanation for the manner in which genetic algorithms operate. BioSystems 41(1): 1–15
Beyer H-G, Schwefel H-P, Wegener I (2002) How to analyse evolutionary algorithms. Theor Comput Sci 287(1): 101–130
Schwefel H-P (1993) Evolution and optimum seeking: the sixth generation. Wiley, New York
Goldberg D, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. Found Genet algorithm 1: 69–93
Wojtusiak J, Michalski RS (2006) The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems. In: GECCO ’06: Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM Press, USA, pp 1281–1288
Leung Y-W, Wang Y (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans Evol Comput 5: 41–53
Yao X, Liu Y (1997) Fast evolution strategies. Control Cybern 26: 467–496
Nguyen QH, Ong YS, Krasnogor N (2007) A study on the design issues of memetic algorithm. In: IEEE congress on evolutionary computation (CEC), 25–28 September 2007, pp 2390–2397
Tavares J, Pereira FB, Costa E (2008) Multidimensional knapsack problem: a fitness landscape analysis. IEEE Trans Syst Man Cybern Part B Cybern 38(3): 604–616
Jones T, Forrest S (1995) Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Proceedings of the 6th international conference on genetic algorithms, pp 184–192
Shang Y-W, Qiu Y-H (2006) A note on the extended Rosenbrock function. Evol Comput 14(1): 119–126
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Le, M.N., Ong, YS., Jin, Y. et al. Lamarckian memetic algorithms: local optimum and connectivity structure analysis. Memetic Comp. 1, 175–190 (2009). https://doi.org/10.1007/s12293-009-0016-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12293-009-0016-9