Abstract
How might domain knowledge constrain a genetic algorithm and systematically impact the algorithm’s traversal of the search space? In particular, in this paper the hypothesis is advanced that a semantic tree of financial knowledge can be used to influence the results of a genetic algorithm for financial investing problems. An algorithm is described, called the “Memetic Algorithm for Domain Knowledge”, and is instantiated in a software system. In mutation experiments, this system chooses financial ratios to use as inputs to a neural logic network which classifies stocks as likely to increase or decrease in value. The mutation is guided by a semantic tree of financial ratios. In crossover experiments, this system solves a portfolio optimization problem in which components of an individual represent weights on stocks; knowledge in the form of a semantic tree of industries determines the order in which components are sorted in individuals. Both synthetic data and real-world data are used. The experimental results show that knowledge can be used to reach higher fitness individuals more quickly. More interestingly, the results show how conceptual distance in the human knowledge can correspond to distance between evolutionary individuals and their fitness. In other words, knowledge might be dynamically used to at times increase the step size in a search algorithm or at times to decrease the step size. These results shed light on the role of knowledge in evolutionary computation and are part of the larger body of work to delineate how domain knowledge might usefully constrain the genetic algorithm.
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References
Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6(5): 443–462
Angeline P (1997) Subtree crossover: building block engine or macromutation? In: Jea K (ed) Proceedings of the second annual genetic programming conference. Stanford University/Morgan Kaufmann, pp 9–17
Aranha C, Iba H (2009) The memetic tree-based genetic algorithm and its application to portfolio optimization. Memetic Comp 1(2): 139–151
Berry B (2004) Editorial. Intell Syst Account Financ Manag 12(1): 1–4
Bhattacharyya M, Bandyopadhyay S (2009) Solving maximum fuzzy clique problem with neural networks and its applications. Memetic Comp 1(4): 281–290. doi:10.1007/s12293-009-0019-6
Bhattacharyya S, Pictet OV, Zumbach G (2002) Knowledge-intensive genetic discovery in foreign exchange markets. IEEE Trans Evol Comput 6(2): 169–181
Bonissone P, Subbu R, Eklund N, Kiehl T (2006) Evolutionary algorithms + domain knowledge = real-world evolutionary computation. IEEE Trans Evol Comput 10(3): 256–280
Buriol LS, Resende MGC, Ribeiro CC, Thorup M (2002) A memetic algorithms for OSPF routing. In: Proceedings of the 6th INFORMS telecommunications conference, Boca Raton, Florida, pp 187–188
Cacciola M, Megali G, Fiasché M, Versaci M, Morabito FC (2010) A comparison between neural networks and k-nearest neighbours for blood cells taxonomy. Memetic Comp 2(3): 237–246
Chen AP, Chen MY (2006) Integrating extended classifier system and knowledge extraction model for financial investment prediction: an empirical study. Expert Syst Appl 31(1): 174–183
Chen SH (2002) Genetic algorithms and genetic programming in computational finance. Kluwer, Boston
Conrad M (1979) Bootstrapping on the adaptive landscape. BioSystems 11(2–3): 167–182
Dawkins R (1989) The selfish gene. 2 edn. Oxford University Press, New York
De Jong ED, Watson RA, Thierens D (2005) On the complexity of hierarchical problem solving. In: Proceedings of the genetic and evolutionary computation conference, Washington DC, 2005. ACM, pp 1201–1208
De Jong KA (2006) Evolutionary computation: a unified approach. MIT, Cambridge
De Jong KA, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 13(2–3): 161–188
Du J, Rada R (2010) Training a neural logic network to predict financial returns: a case study. Int J Electron Finance 4(1): 19–38
FTSE (2011) Industry classification benchmark production specification. FTSE International Limited, London. http://www.icbenchmark.com/ICBDocs/ProductSpec_02_2008.pdf
Giraldez R, Aguilar-Ruiz JS, Riquelme JC (2005) Knowledge-based fast evaluation for evolutionary learning. IEEE Trans Syst Man Cybern Part C 35(2): 254–261
Goldberg DE (1989) Genetic algorithms in optimization, search and machine learning. Addison-Wesley, Reading
He J, Yao X, Li J (2005) A comparative study of three evolutionary algorithms incorporating different amounts of domain knowledge for node covering problem. IEEE Trans Syst Man Cybern Part C 35(2): 266–271
Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Kim K-j (2004) Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures. Intell Syst Account Financ Manag 12(2): 167–176
Kim MK, Han I, Lee KC (2004) Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea Stock Price Index. Intell Syst Account Financ Manag 12(1): 43–60
Klein MR, Methlie LB (1995) Knowledge-based decision support systems with applications in business. 2 edn. Wiley, New York
Knoblock CA (1994) Automatically generating abstractions for planning. Artif Intell 68(2): 243–302
Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput 9(5): 474–488
Krasnogor N, Smith J (2008) Memetic algorithms: the polynomial local search complexity theory perspective. J Math Model Algorithms 7(1): 3–24
Lin Y, Bhanu B (2005) Evolutionary feature synthesis for object recognition. IEEE Trans Syst Man Cybern Part C 35(2): 156–171
Lumanpauw E, Pasquier M, Chai Q (2007) MNFS-FPM: a novel memetic neuro-fuzzy system based financial portfolio management. In: IEEE congress on evolutionary computation, Singapore, pp 2554–2561
Matsatsinis NF, Doumpos M, Zopounidis C (1997) Knowledge acquisition and representation for expert systems in the field of financial analysis. Expert Syst Appl 12(2): 247–262
Mauttone A, Urquhart ME (2009) A multi-objective metaheuristic approach for the transit network design problem. Public Transp 1(4): 253–273
McPhee NF, Ohs B, Hutchison T (2008) Semantic building blocks in genetic programming. In: Proceedings of the 11th European conference on genetic programming, Naples, 2008. Springer, Berlin, pp 134–145
Moraglio A, Borenstein Y (2009) A gaussian random field model of smooth fitness landscapes. In: Foundations of genetic algorithms, Orlando, 2009. ACM, New York, pp 171–182
Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Report 826, Caltech Concurrent Computation Program. California Institute of Technology, Pasadena
Moscato P (1999) Memetic algorithms: a short introduction. In: Corne D, Glover F, Dorigo M (eds) New ideas in optimization. McGraw-Hill, New York, pp 219–234
Nguyen QH, Ong YS, Krasnogor N (2007) A study on the design issues of memetic algorithm. In: Proceedings of IEEE Congress on evolutionary computation, Singapore, pp 2390–2397
Nguyen QH, Ong YS, Lim MH (2009) A probabilistic memetic framework. IEEE Trans Evol Comput 13(3): 604–623
O’Neill M, Ryan C, Keijzer M, Cattolico M (2003) Crossover in grammatical evolution. Genet Program Evol Mach 4(1): 67–93. doi:10.1023/a:1021877127167
Oh K, Kim TY, Min S-H, Lee HY (2006) Portfolio algorithm based on portfolio beta using genetic algorithm. Expert Syst Appl 30(3): 527–534
Ong Y-S, Lim M-H, Zhu N, Wong K-W (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern Part B 36(1): 141–152
Otero F, Freitas A, Johnson C (2010) A hierarchical multi-label classification ant colony algorithm for protein function prediction. Memetic Comp 2(3): 165–181. doi:10.1007/s12293-010-0045-4
Patterson DW (1990) Introduction to artificial intelligence and expert systems. Prentice-Hall, Englewood Cliffs
Rada R (1991) Computers and gradualness: the selfish meme. AI & Society 5(3): 246–254
Rada R (2008) Expert systems and evolutionary computing for financial investing: a review. Expert Syst Appl 34(4): 2232–2240
Rada R, Mili H, Bicknell E, Blettner M (1989) Development and application of a metric on semantic nets. IEEE Trans Syst Man Cybern 19(1): 17–30
Ryan C, Collins JJ, Neill MO (1998) Grammatical evolution: evolving programs for an arbitrary language. In: Banzhaf W, Poli R, Schoenauer M, Fogarty TC (eds) First European workshop on genetic programming. Springer, Berlin, pp 83–95
Sattar A, Seguier R (2010) HMOAM: hybrid multi-objective genetic optimization for facial analysis by appearance model. Memetic Comp 2(1):25–46. doi:10.1007/s12293-010-0038-3
Slagle JR, Chin-Liang C, Lee RCT (1970) A new algorithm for generating prime implicants. IEEE Trans Comput C 19(4): 304–310
Streichert F, Tanaka-Yamawaki M (2006) The effect of local search on the constrained portfolio selection problem. In: IEEE Congress on evolutionary computation, Vancouver, BC, pp 2368–2374
Teh H-H (1995) Neural logic networks. World Scientific, Singapore
Tsakonas A, Dounias G, Doumpos M, Zopounidis C (2006) Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming. Expert Syst Appl 30(3): 449–461
Vose MD (1999) The simple genetic algorithm: foundations and theory. MIT, Cambridge
Wang J-H, Leu J-Y (1996) Stock market trend prediction using ARIMA-based neural networks. In: Proceedings of 1996 IEEE international conference on neural networks, Washington, DC, pp 2160–2165
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1): 67–82
Yu L, Wang S, Lai KK (2006) An integrated data preparation scheme for neural network data analysis. IEEE Trans Knowl Data Eng 18(2): 217–230
Zopounidis C, Doumpos M (2000) Intelligent decision aiding systems based on multiple criteria for financial engineering. Kluwer, Boston
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Du, J., Rada, R. Memetic algorithms, domain knowledge, and financial investing. Memetic Comp. 4, 109–125 (2012). https://doi.org/10.1007/s12293-012-0079-x
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DOI: https://doi.org/10.1007/s12293-012-0079-x