Skip to main content
SpringerLink
Log in

An algorithm development environment for problem-solving: software review

  • Software Review
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

Algorithm Development Environment for Problem Solving (ADEP) is a development platform catering to the needs of designing and exploring computationally viable configurations of metaheuristic algorithms. It is motivated by the lack of tools capable of capitalizing on the richness of memetic computing techniques that surfaced in recent years. This software review article introduces the functional features of ADEP and describes the the various utility modules within the ADEP metaheuristics framework, in particular the LVRP tree data structure, configuration and simulation visualization, and the automated configuration via the problem-driven learning engine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen XS, Ong YS, Lim MH, Tan KC (2011) A multi-facet survey of memetic computing. IEEE Trans Evol Comput 15(5): 591–607

    Article  Google Scholar 

  2. Lim MH, Krasnogor N, Ong YS, Gustafson S (2012) Editorial, Memetic Comput 4:1

  3. Lim MH, Gustafson S, Krasnogor N, Ong YS (2009) Editorial. Memetic Comput 1(3): 173–174

    Article  Google Scholar 

  4. Voudouris C, Dorne R, Lesaint D, Liret A (2001) iopt: A software toolkit for heuristic search methods. In: CP01, Paphos, pp 716–729. Springer, Berlin

  5. Gaspero LD, Schaerf A (2000) Easylocal++: An object-oriented framework for flexible design of local search algorithms. Softw Practice Experience 33(8): 733–765

    Article  Google Scholar 

  6. Fink A, Voβ S (2002) Hotframe: a heuristic optimization framework. Optimization Software Class Libraries, OR/CS Interfaces Series. Kluwer Academic Publishers, Boston, pp 81–154

  7. Jones M, McKeown G, Rayward-Smith V (1998) Templar: An object-oriented framework for distributed combinatorial optimzation. In: Proceedings of the UNICOM Seminar on Modern Heuristics for Decision Support. UNICOM Ltd, Brunei University, UK

  8. Schaerf A, Lenzerini M, Cadoli M (Jul 1999) Local++: a C++ framework for local search algorithms. In: Proceedings of technology of object-oriented languages and systems, pp 152–161

  9. Andreatta AA, Carvalho SER, Ribeiro CC (1998) An object-oriented framework for local search heuristics. In: Proceedings of the technology of object-oriented languages and systems (TOOLS ’98). IEEE Computer Society, Washington, DC, pp 33

  10. Shaw P, Backer BD, Furnon V (2002) Improved local search for CP toolkits. Ann Oper Res 115(1):31–50

    Google Scholar 

  11. Collet P, Lutton E, Schoenauer M, Louchet J (Sept 2000) Take it EASEA. In: Lecture notes in computer science, vol 1917 archive. Proceedings of the 6th international conference on parallel problem solving

  12. Veenhuis C, Franke K, Koppen M (2000) A semantic model for evolutionary computation. In: Proceedings of 6th international conference on soft computing

  13. Wall M (1996) GAlib: A C++ library of genetic algorithm components. Mechanical Engineering Department. Massachusetts Institute of Technology

  14. Keijzer M, Merelo JJ, Romero G, Schoenauer M (2001) Evolving objects: a general purpose evolutionary computation library. In: EA-01, evolution artificielle, 5th international conference in evolutionary algorithms, pp 231–244

  15. HEEDS (2002) Hierarchical Evolutionary Engineering Design System. Getting Started Manual

  16. Lassila O (1996) Ozone temporal constraint propagator. Technical Report CMU-RI-TR-96-12, Robotics Institute, Pittsburgh

  17. Birattari M, Stutzle T, Paquete L, Varrentrapp K (2002) A racing algorithm for configuring metaheuristics. In: Proceedings of the genetic and evolutionary computation conference, pp 11–18. Morgan Kaufmann, San Fransisco

  18. Stanley KO, Bryant BD, Miikkulainen R (2005) Real-time neuroevolution in the nero video game. IEEE Trans Evol Comput 9(6): 653–668

    Article  Google Scholar 

  19. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. complex adaptive systems. The MIT Press, Cambridge

    Google Scholar 

  20. Burke E, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Handbook of metaheuristics, International series in operations research & management science, Chap. 16, pp 457–474

  21. Ong YS, Keane AJ (2004) Meta-lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2): 99–110

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. Nguyen QH, Ong YS, Lim MH (2008) Non-genetic transmission of memes by diffusion. In: GECCO’08: proceedings of the 10th annual conference on genetic and evolutionary computation, pp 1017–1024. ACM, New York

  24. Dawkins R (1989) The selfish gene. Clarendon Press, Oxford

    Google Scholar 

  25. Krasnogor N, Smith J (2001) Emergence of protable search strategies based on a simple inheritance mechanism. In: The genetic and evolutionary computation conference, pp 432–439. San Francisco

  26. Smith J (2002) Co-evolving memetic algorithms: initial investigations. In: Parallel problem solving from nature—PPSN VII, pp 537–548

  27. Krasnogor N (1999) Co-evolution of genes and memes in memetic algorithms. In: Proceedings of the 1999 genetic and evolutionary computation conference workshop program, p 371

  28. Krasnogor N, Gustafson S (2002) Toward truly ”‘memetic”’ memetic algorithms: discussion and proof of concepts. In: Advances in nature-inspired computation: the PPSN VII workshops, pp 21–22

  29. Krasnogor N, Gustafson S (2004) A study on the use of “self-generation” in memetic algorithms. Nat Comput 3(1): 53–76

    Article  MathSciNet  MATH  Google Scholar 

  30. Lim KK, Ong YS, Lim MH, Chen XS, Agarwal A (Nov 2007) Hybrid Ant colony algorithm for path planning in Sparse graphs. Soft Comput J 981–994

  31. Agarwal A, Xu YL, Lim MH (2003) Graph compression via compaction of degree-2 nodes for urav visitation sequencing. Unmanned Vehicle System Technologies

  32. Tang J, Lim MH, Ong YS (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput J 11(9): 873–888

    Article  Google Scholar 

  33. Tang J, Lim MH, Ong YS (2006) Parallel memetic algorithm with selective local search for large scale quadratic assignment problems. Int J Innov Comput Inf Control 2(6): 1399–1416

    Google Scholar 

  34. Tang J, Lim MH, Ong YS (2006) Adaptation for parallel memetic algorithm based on population entropy. In: Proceedings of the 8th annual conference on genetic and evolutionary computation (GECCO), Seattle, Washington, pp 575–582 (Best paper Award Nomination)

  35. Burkard RE, Karisch SE, Rendl F (1997) QAPLIB—a quadratic assignment problem library. J Glob Optim 10(4): 391–403

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Systems Centre, 50 Nanyang Drive, Research Techno Plaza, Level 7, BorderX Block, Singapore, 637553, Singapore

    Xianshun Chen

Authors
  1. Xianshun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xianshun Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, X. An algorithm development environment for problem-solving: software review. Memetic Comp. 4, 149–161 (2012). https://doi.org/10.1007/s12293-012-0083-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-012-0083-1

Keywords

Search

Navigation