Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
An introduction to quantum computing for non-physicists
ACM Computing Surveys (CSUR)
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Linear Genetic Programming (Genetic and Evolutionary Computation)
Linear Genetic Programming (Genetic and Evolutionary Computation)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Algorithms for quantum computation: discrete logarithms and factoring
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
Evolving CUDA PTX programs by quantum inspired linear genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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The huge performance superiority of quantum computers for some specific problems lies in their direct use of quantum mechanical phenomena (e.g. superposition of states) to perform computations. This has motivated the creation of quantum-inspired evolutionary algorithms (QIEAs), which successfully use some quantum physics principles to improve the performance of evolutionary algorithms (EAs) for classical computers. This paper proposes a novel QIEA (Quantum-Inspired Linear Genetic Programming - QILGP) for automatic synthesis of machine code (MC) programs and aims to present a preliminary evaluation of applying the quantum-inspiration paradigm to evolve programs by using two symbolic regression problems. QILGP performance is compared to AIMGP model, since it is the most successful genetic programming technique to evolve MC. In the first problem, the hit ratio of QILGP (100%) is greater than the one of AIMGP (77%). In the second problem, QILGP seems to carry on a less greedy search than AIMGP. Since QILGP presents some satisfactory results, this paper shows that the quantum-inspiration paradigm can be a competitive approach to evolve programs more efficiently, which encourages further developments of that first and simplest QILGP model with multiple individuals.