Abstract
A new model for evolving evolutionary algorithms (EAs) is proposed in this paper. The model is based on the multi expression programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme which is used for solving a particular problem. Several evolutionary algorithms for function optimization are evolved by using the considered model. The evolved evolutionary algorithms are compared with a human-designed genetic algorithm. Numerical experiments show that the evolved evolutionary algorithms can compete with standard approaches for several well-known benchmarking problems.
Similar content being viewed by others
References
Aho A, Sethi R, Ullman J (1986) Compilers: principles, techniques, and tools. Addison wesley, Reading
Angeline PJ (1995) Adaptive and self-adaptive evolutionary computations, computational intelligence: a dynamic systems perspective. IEEE Press, New York, pp 152–163
Angeline PJ (1996) Two self-adaptive crossover operators for genetic programming. In: Angeline P, Kinnear KE (eds) Advances in genetic programming II. MIT Press, Cambridge, pp 89–110
Back T (1992) Self-adaptation in genetic algorithms. In: Toward a practice of autonomous systems: proceedings of the first european conference on artificial life. MIT Press, Cambridge, pp 263–271
Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming – an introduction on the automatic evolution of computer programs and its applications. dpunkt/Morgan Kaufmann, Heidelberg/San Francisco
Beyer HG (1994) Toward a theory of evolution stategies: the (μ, λ) strategy. Evol Comput 2(4):381–408
Brameier M, Banzhaf W (2001) A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans Evol Comput 15:17–26
Brameier M, Banzhaf W (2002) Explicit control of diversity and effective variation distance in linear genetic programming. In: Lutton E, Foster J, Miller J, Ryan C, Tettamanzi A (eds) European conference on genetic programming IV. Springer, Berlin Hedilberg New York, pp 38–50
Brameier M, Banzhaf W (2001) Evolving teams of predictors with linear genetic programming. Genetic Program Evolv Mach 2:381–407
Burkard RE, Rendl F (1991) QAPLIB-A quadratic assignment problem libray. Eur J Oper Res 55:115–119
Cormen TH, Leiserson CE, Rivest RR (1990) Introduction to algorithms. MIT Press, Cambridge
Edmonds B (2001) Meta-genetic programming: co-evolving the operators of variation. Electrik AI 9:13–29
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(1):87–129
Freisleben B, Merz P (1996) A genetic local search algorithm for solving symmetric and asymmetric traveling Salesman Problems. In: IEEE international conference on evolutionary computation. IEEE Press, pp 616–621
Garey MR, Johnson DS (1979) Computers and intractability: a guide to NP-completeness. Freeman & Co, San Francisco
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Koza JR (1992) Genetic programming, on the programming of computers by means of natural selection. MIT Press, Cambridge
Koza JR (1994) Genetic programming II, automatic discovery of reusable subprograms. MIT Press, Cambridge
Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. PhD Thesis, University of the West of England
Merz P, Freisleben B (1997) Genetic local search for the TSP: new results. In: IEEE international conference on evolutionary computation. IEEE Press, pp 616–621
Merz P, Freisleben B (2000) Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans Evol Comput 4:337–352
Nordin P (1994) A Compiling genetic programming system that directly manipulates the machine-code. In: Kinnear KE (eds) Advances in genetic programming I. MIT Press, Cambridge, pp 311–331
Oltean M, Groşan C (2003) A comparison of several linear genetic programming techniques. Complex-Systems 14(4):282–311
Oltean M, Groşan C (2003) Evolving evolutionary algorithms using multi expression programming. In: Banzhaf W et al. (eds) European conference on artificial life VII, LNAI 2801. Springer, Berlin Hedilberg New York, pp 651–658
Oltean M (2003) Solving even-parity problems with multi expression programming. In: Chen K et al. (eds) International workshop on frontiers in evolutionary algorithm V. Triangle Research Park, pp 315–318
Oltean M (2003) Evolving evolutionary algorithms for function optimization. In: Chen K et al. (eds) International workshop on frontiers in evolutionary algorithm V. Triangle Research Park, pp 295–298
Oltean M, Dumitrescu D (2004) Evolving TSP heuristics with multi expression programming. In: Bubak M et al. (eds) International conference on computational sciences ICCS’04 Vol II. Springer, Berlin Hedilberg New York, pp 675–678
Oltean M (2005) Evolving evolutionary algorithms using linear genetic programming. Evol Comput 13(3):387–410
Prechelt L (1994) PROBEN1 - A set of neural network problems and Benchmarking rules, Technical Report 21. University of Karlsruhe, Germany
Reinelt G (1991) TSPLIB - A traveling salesman problem library, ORSA. J Comput 3:376–384
Spector L, Robinson A (2002) Genetic programming and autoconstructive evolution with the push programming language. Genetic Programm Evol Mach 3(1):7–40
Stephens CR, Olmedo IG, Vargas JM, Waelbroeck H (1998) Self-adaptation in evolving systems. Artif Life 4:183–201
Syswerda G (1989) Uniform crossover in genetic algorithms. In: Schaffer JD (eds) The 3rd International conference on genetic algorithms. Morgan Kaufmann Publishers, San Mateo, pp 2–9
Teller A (1996) Evolving programmers: the co-evolution of intelligent recombination operators. In: Angeline P, Kinnear KE (eds) Advances in genetic programming II. MIT Press, Cambridge, pp 45–68
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102
Wolpert DH, McReady WG (1995) No Free Lunch Theorems for search. technical report SFI-TR-05-010, Santa Fe Institute, USA
Wolpert DH, McReady WG (1997) No Free Lunch Theorems for optimization. IEEE Trans Evol Comput, pp 67–82
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Oltean, M. Evolving Evolutionary Algorithms with Patterns. Soft Comput 11, 503–518 (2007). https://doi.org/10.1007/s00500-006-0079-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-006-0079-1