ABSTRACT
The only parameter that dependency structure matrix genetic algorithm II (DSMGA-II) requires is the population size, and the practicability of DSMGA-II would be further enhanced by removing the parameter. Existing parameterless schemes cannot be directly applied to DSMGA-II due to the confliction with the back mixing---one of the major operators of DSMGA-II. This paper focused on developing such parameterless schemes for DSMGA-II. Empirically these scheme yields promising results.
- P. A. Bosman and D. Thierens. Linkage neighbors, optimal mixing and forced improvements in genetic algorithms. In Proceedings of the 14th annual conference on Genetic and evolutionary computation, pages 585--592. ACM, 2012. Google ScholarDigital Library
- B. W. Goldman and W. F. Punch. Parameter-less population pyramid. In Proceedings of the 2014 conference on Genetic and evolutionary computation, pages 785--792. ACM, 2014. Google ScholarDigital Library
- G. R. Harik and F. G. Lobo. A parameter-less genetic algorithm. In GECCO, volume 99, pages 258--267, 1999. Google ScholarDigital Library
- S.-H. Hsu and T.-L. Yu. Optimization by pairwise linkage detection, incremental linkage set, and restricted/back mixing: Dsmga-ii. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, pages 519--526. ACM, 2015. Google ScholarDigital Library
- M. Pelikan and T.-K. Lin. Parameter-less hierarchical boa. In Genetic and Evolutionary Computation-GECCO 2004, pages 24--35. Springer, 2004.Google ScholarCross Ref
Index Terms
- Investigation on Parameterless Schemes for DSMGA-II
Recommendations
Parameter-less, population-sizing DSMGA-II
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionLimiting the number of required settings is an important part of any evolutionary method development. The final objective of this process is a method version that is parameter-less. Based on the research results presented that far, the leading methods ...
Theoretical Perspective of Convergence Complexity of Evolutionary Algorithms Adopting Optimal Mixing
GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary ComputationThe optimal mixing evolutionary algorithms (OMEAs) have recently drawn much attention for their robustness, small size of required population, and efficiency in terms of number of function evaluations (NFE). In this paper, the performances and behaviors ...
Investigation of the exponential population scheme for genetic algorithms
GECCO '18: Proceedings of the Genetic and Evolutionary Computation ConferenceEarly development of GAs requires many parameters to be tuned. The tuning process increases the difficulty for inexperienced practitioners. Modern GAs have most of these parameters pre-determined, and therefore recent research concerning parameterless ...
Comments