Erik Hemberg
Una-May O'Reilly
ABSTRACT
We present how we have strategically allocated fitness evaluations in a large-scale rule based evolutionary system called ECStar. We describe a strategy that culls potentially weaker solutions early, then later only compete with solutions which have equivalent fitness evaluations, as they are evaluated on more fitness cases. Despite incurring some imprecision in fitness comparison, which arises from not evaluating on all the fitness cases or even the same ones, the strategy allows our system to make effective progress when the resources at its disposal are unpredictably available.
- Hornby, G.: Alps: the age-layered population structure for reducing the problem of premature convergence. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation. pp. 815--822. ACM (2006) Google Scholar
Digital Library
- Saeed, M., Lieu, C., Raber, G., Mark, R.: Mimic ii: a massive temporal icu patient database to support research in intelligent patient monitoring. In: Computers in Cardiology, 2002. pp. 641--644. IEEE (2002)Google Scholar
- Urbanowicz, R., Moore, J.: Learning classifier systems: a complete introduction, review, and roadmap. Journal of Artificial Evolution and Applications 2009, 1 (2009) Google ScholarCross Ref
Recommendations
Fitness tracking based evolutionary programming: a novel approach for function optimization
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computationIn order to achieve a satisfactory optimization performance by evolutionary programming (EP), it is necessary to ensure proper balance between exploration and exploitation. It is obvious that one single mutation operator is not the answer. Moreover, ...
Using holey fitness landscapes to counteract premature convergence in evolutionary algorithms
GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computationPremature convergence is a persisting problem in evolutionary optimisation, in particular - genetic algorithms. While a number of methods exist to approach this issue, they usually require problem specific calibration or only partially resolve the issue,...
Large-Scale Evolutionary Strategy Based on Gradient Approximation
For large-scale optimization, CMA-ES has the disadvantages of high complexity and premature stagnation. An improved CMA-ES algorithm called GI-ES was proposed in this paper. For the problem of high complexity, the method in this paper replaces the ...
Comments