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Long Term Memory Assistance for Evolutionary Algorithms

Author

Listed:
  • Matej Črepinšek

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia)

  • Shih-Hsi Liu

    (Department of Computer Science, California State University Fresno, Fresno, CA 93740, USA)

  • Marjan Mernik

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia)

  • Miha Ravber

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia)

Abstract

Short term memory that records the current population has been an inherent component of Evolutionary Algorithms (EAs). As hardware technologies advance currently, inexpensive memory with massive capacities could become a performance boost to EAs. This paper introduces a Long Term Memory Assistance (LTMA) that records the entire search history of an evolutionary process. With LTMA, individuals already visited (i.e., duplicate solutions) do not need to be re-evaluated, and thus, resources originally designated to fitness evaluations could be reallocated to continue search space exploration or exploitation. Three sets of experiments were conducted to prove the superiority of LTMA. In the first experiment, it was shown that LTMA recorded at least 50 % more duplicate individuals than a short term memory. In the second experiment, ABC and jDElscop were applied to the CEC-2015 benchmark functions. By avoiding fitness re-evaluation, LTMA improved execution time of the most time consuming problems F 03 and F 05 between 7% and 28% and 7% and 16%, respectively. In the third experiment, a hard real-world problem for determining soil models’ parameters, LTMA improved execution time between 26% and 69%. Finally, LTMA was implemented under a generalized and extendable open source system, called EARS. Any EA researcher could apply LTMA to a variety of optimization problems and evolutionary algorithms, either existing or new ones, in a uniform way.

Suggested Citation

  • Matej Črepinšek & Shih-Hsi Liu & Marjan Mernik & Miha Ravber, 2019. "Long Term Memory Assistance for Evolutionary Algorithms," Mathematics, MDPI, vol. 7(11), pages 1-25, November.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:11:p:1129-:d:288150
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    References listed on IDEAS

    as
    1. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
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    Cited by:

    1. Boštjan Slivnik & Željko Kovačević & Marjan Mernik & Tomaž Kosar, 2022. "On Comprehension of Genetic Programming Solutions: A Controlled Experiment on Semantic Inference," Mathematics, MDPI, vol. 10(18), pages 1-17, September.

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