Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Foundations of genetic programming
Foundations of genetic programming
An Analysis of the Causes of Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
Modification point depth and genome growth in genetic programming
Evolutionary Computation
Implicit manipulation of polynomials using zero-suppressed BDDs
EDTC '95 Proceedings of the 1995 European conference on Design and Test
Context-aware mutation: a modular, context aware mutation operator for genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
On the limiting distribution of program sizes in tree-based genetic programming
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Semantics based crossover for boolean problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A fine-grained view of GP locality with binary decision diagrams as ant phenotypes
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Promoting phenotypic diversity in genetic programming
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
Geometric semantic genetic programming
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Genetic Programming and Evolvable Machines
Expert Systems with Applications: An International Journal
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Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic changes in programs that result from the mutation operation. Using semantically driven mutation, we demonstrate increased performance in genetic programming on seven benchmark genetic programming problems over two different domains.