Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Hierarchical learning with procedural abstraction mechanisms
Hierarchical learning with procedural abstraction mechanisms
The genetic algorithm as a discovery engine: strange circuits and new principles
Creative evolutionary systems
Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Genetic Programming Experiments with Standard and Homologous Crossover Methods
Genetic Programming and Evolvable Machines
An Evolved Circuit, Intrinsic in Silicon, Entwined with Physics
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Explorations in design space: unconventional electronics designthrough artificial evolution
IEEE Transactions on Evolutionary Computation
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A novel genetic operator, the plagiarism operator, is introduced for evolutionary design and optimisation. This operator is analogous in some respects to crossover and to biological transposition. Plagiarism is shown to be theoretically superior to uniform mutation for generalised counting-ones problems, and also to outperform uniform mutation on certain classes of random fitness landscapes. Experimental results are presented showing that plagiarism speeds up the artificial evolution of certain digital logic circuits. The performance of this operator is interpreted in terms of the non-uniform distribution of genetic primitives in good solutions for certain problems.