Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Vector-valued function estimation by grammatical evolution for autonomous robot control
Information Sciences: an International Journal
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Grammatical Evolution (GE) is one of the evolutionary algorithms to find functions and programs, which can deal according to a syntax with tree structure by one-dimensional chromosome of Genetic Algorithm. An original GE starts from the definition of the syntax by means of Backus Naur Form (BNF). Chromosome in binary number is translated to that in decimal number. The BNF syntax selects according to the remainder of the decimal number with respect to the total number of candidate rules. In this study, we will introduce three schemes for improving the convergence property of the original GE. In numerical examples, the original GE is compared in function identification problem with the Genetic Programming (GP), which is one of the most popular evolutionary algorithm to find unknown functions or programs. Three algorithms are compared in Santa Fe trail problem and prediction problem of Nikkei stock average, which finds programs to control artificial ants collecting foods. The results show that the efficiency of schemes depends on the problem to be solved and that the schemes 1 and 2 are effective for Santa Fe trail problem and prediction problem of Nikkei stock average, respectively.