Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Crossover in Grammatical Evolution: The Search Continues
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Ripple Crossover in Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Redundant representations in evolutionary computation
Evolutionary Computation
Identifying structural mechanisms in standard genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
What makes a problem GP-hard? validating a hypothesis of structural causes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Grammatical evolution tutorial
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Design of Modern Heuristics: Principles and Application
Design of Modern Heuristics: Principles and Application
IEEE Transactions on Evolutionary Computation
Representation and structural difficulty in genetic programming
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
Genetic programming (GP) has problems with structural difficulty as it is unable to search effectively for solutions requiring very full or very narrow trees. As a result of structural difficulty, GP has a bias towards narrow trees which means it searches effectively for solutions requiring narrow trees. This paper focuses on the structural difficulty of grammatical evolution (GE). In contrast to GP, GE works on variable-length binary strings and uses a grammar in Backus-Naur Form (BNF) to map linear genotypes to phenotype trees. The paper studies whether and how GE is affected by structural difficulty. For the analysis, we perform random walks through the search space and compare the structure of the visited solutions. In addition, we compare the performance of GE and GP for the Lid problem. Results show that GE representation is biased, this means it has problems with structural difficulty. For binary trees, GE has a bias towards narrow and deep structures; thus GE outperforms standard GP if optimal solutions are composed of very narrow and deep structures. In contrast, problems where optimal solutions require more dense trees are easier to solve for GP than for GE.