Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Extracted global structure makes local building block processing effective in XCS
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Sub-structural niching in estimation of distribution algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Do not match, inherit: fitness surrogates for genetics-based machine learning techniques
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
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This paper reviews a competentPittsburgh LCS that automatically minesimportant substructures of the underlying problems and takes problems that were intractablewith first-generation Pittsburgh LCS and renders them tractable. Specifically, we propose a 茂戮驴-ary extended compact classifier system (茂戮驴eCCS) which uses (1) a competent genetic algorithm (GA) in the form of 茂戮驴-ary extended compact genetic algorithm, and (2) a niching method in the form restricted tournament replacement, to evolve a set of maximally accurate and maximally general rules. Besides showing that linkage exist on the multiplexer problem, and that 茂戮驴eCCS scales exponentially with the number of address bits (building block size) and quadratically with the problem size, this paper also explores non-traditional rule encodings. Gene expression encodings, such as the Karva language, can also be used to build 茂戮驴eCCS probabilistic models. However, results show that the traditional ternary encoding 0,1,#presents a better scalability than the gene expression inspired ones for problems requiring binary conditions.