We propose a lossless algorithm to compress the information contained in DNA sequences. None of the available universal algorithms compress such data. This is due to the speciicity of genetic information. Our method is based on regularities, such as the presence of palindromes, in the DNA. The results we obtain, although not satisfactory, are far beyond classical algorithms.

In this paper, we investigate the convergence of empirical processes for a class of interacting particle numerical schemes arising in biology, genetic algorithms and advanced signal processing. The Glivenko-Cantelli and Donsker theorems presented in this work extend the corresponding statements in the classical theory and apply to a class of genetic type particle numerical schemes of the non-li...

It is an important and a complicated task to investigate the convergence of a new genetic algorithm based on quantum mechanics concepts including qubits and superposition of states, namely Quantum Genetic Algorithm, in the field of evolutionary computation. This paper analyzes convergence property of quantum genetic algorithm which uses its special quantum operator instead of canonical operator...

Genetic Algorithm does not increase as fast as the classical constrained optimisation algorithm. A hybrid scheme is also introduced, combining the benefits of both algorithms. It exhibited better overall convergence rate but the same solution as CCO-BAVP.

We show how to apply the recursive quantum approximate optimization algorithm (RQAOA) MAX-k-CUT, problem of finding an k-vertex coloring a graph. compare this proposal best known classical and hybrid classical-quantum algorithm...