Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Programming and Evolvable Machines
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evaluating GP schema in context
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A less destructive, context-aware crossover operator for GP
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
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We introduce potential fitness, a variant of fitness function that operates in the space of schemata and is applicable to tree-based genetic programing. The proposed evaluation algorithm estimates the maximum possible gain in fitness of an individual's direct offspring. The value of the potential fitness is calculated by analyzing the context semantics and subtree semantics for all contexts (schemata) of the evaluated tree. The key feature of the proposed approach is that a tree is rewarded for the correctly classified fitness cases, but it is not penalized for the incorrectly classified ones, provided that such errors are recoverable by substitution of an appropriate subtree (which is however not explicitly considered by the algorithm). The experimental evaluation on a set of seven boolean benchmarks shows that the use of potential fitness may lead to better convergence and higher success rate of the evolutionary run.