Introducing an Age-Varying Fitness Estimation Function

  • Babak HodjatEmail author
  • Hormoz Shahrzad
Part of the Genetic and Evolutionary Computation book series (GEVO)


We present a method for estimating fitness functions that are computationally expensive for an exact evaluation. The proposed estimation method applies a number of partial evaluations based on incomplete information or uncertainties. We show how this method can yield results that are close to similar methods where fitness is measured over the entire dataset, but at a fraction of the speed or memory usage, and in a parallelizable manner. We describe our experience in applying this method to a real world application in the form of evolving equity trading strategies.

Key words

Evolutionary Computation Genetic Algorithms Fitness Functions Distribution Large Data 


  1. Akbarzadeh-T MR, Mosavat I, Abbasi S (2003) Friendship modeling for cooperative co-evolutionary fuzzy systems: A hybrid ga-gp algorithm. Proceedings of the 22nd International Conference of North American Fuzzy Information Processing Society pp 61–66Google Scholar
  2. Bartlett JE, Kotrlik JW, Higgins CC (2001) Organizational research  Determining appropriate sample size in survey research. Information Technology, Learning, and Performance Journal 19(1)Google Scholar
  3. Bongard JC, Hornby GS (2010) Guarding against premature convergence while accelerating evolutionary search. In: Proceedings of the 12th annual conference on Genetic and evolutionary computationGoogle Scholar
  4. Davarynejad M (2007) Fuzzy Fitness Granulation in Evolutionary Algorithms for complex optimization. Ferdowsi University of Mashhad/Iran, Department of Electrical EngineeringGoogle Scholar
  5. Ducheyne E, Baets BD, deWulf R (2003) Is fitness inheritance useful for real-world applications? Evolutionary Multi-Criterion Optimization, ser LNCS 2631 pp 31–42CrossRefGoogle Scholar
  6. Fitzpatrick JM, Grefenstette JJ (1998) Genetic algorithms in noisy environments. Machine Learning 3(2)Google Scholar
  7. Gaspar-Cunha A, Vieira A (2004) A multi-objective evolutionary algorithm using neural networks to approximate fitness evaluations. International Journal of Computers, Systems, and SignalsGoogle Scholar
  8. Gopalakrishnan G, Minsker B, Goldberg D (2001) Optimal sampling in a noisy genetic algorithm for risk-based remediation design. Bridging the gap: meeting the world’s water and environmental resources challenges Proc world water and environmental resources congressGoogle Scholar
  9. Hornby GS (2005) Alps: the age-layered population structure for reducing the problem of premature convergence. In: In GeccoGoogle Scholar
  10. Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9(1):3–12CrossRefGoogle Scholar
  11. Kosorukoff A (2002) Using incremental evaluation and adaptive choice of operators. Proceedings of Genetic and Evolutionary Computation Conference, GECCO p 688Google Scholar
  12. Mallick D, Lee VCS, Ong YS (2008) An empirical study of genetic programming generated trading rules in computerized stock trading service system. In: International Conference on Service Systems and Service Management, Melbourne, Australia, pp 1–6, DOI doi: 10.1109/ICSSSM.2008.4598507Google Scholar
  13. Myers R, Montgomery D (1995) Response Surface Methodology. John Wiley and Sons, New YorkzbMATHGoogle Scholar
  14. Nelson A (2009) functions in evolutionary robotics: A survey and analysis. Robotics and Autonomous Systems 57(345370)Google Scholar
  15. Sacks J, Welch WJ, Michell TJ, Wynn HP (1989) Design and analysis of computer experiments. Statistical Science 4:409–435MathSciNetzbMATHCrossRefGoogle Scholar
  16. Salami M, Hendtlass T (2003) A fast evaluation strategy for evolutionary algorithms. Appl Soft Comput 2:156–173CrossRefGoogle Scholar
  17. Shiyuan AW, Wu AS, Jin S, Schiavone G, Lin K (2001) An incremental fitness function for partitioning parallel tasks. Proc Genetic and Evolutionary Computation ConferenceGoogle Scholar
  18. Urbanowicz RJ, Moore JH (2009) Learning classifier systems: a complete introduction, review, and roadmap. J Artif Evol App 2009:1–25CrossRefGoogle Scholar
  19. Whitehead B (1996) Genetic evolution of radial basis function coverage using orthogonal niches. IEEE Trans Neural Netw 7(6):1525–1528CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Genetic Finance LLCSan FranciscoUSA

Personalised recommendations