Self-adaptation of mutation distribution in evolutionary algorithms

This paper is posted here with permission from IEEE - Copyright @ 2007 IEEEThis paper proposes a self-adaptation method to control not only the mutation strength parameter, but also the mutation distribution for evolutionary algorithms. For this purpose, the isotropic g-Gaussian distribution is employed in the mutation operator. The g-Gaussian distribution allows to control the shape of the distribution by setting a real parameter g and can reproduce either finite second moment distributions or infinite second moment distributions. In the proposed method, the real parameter q of the g-Gaussian distribution is encoded in the chromosome of an individual and is allowed to evolve. An evolutionary programming algorithm with the proposed idea is presented. Experiments were carried out to study the performance of the proposed algorithm

Comment on "Fock-Darwin States of Dirac Electrons in Graphene-Based Artificial Atoms"

Chen, Apalkov, and Chakraborty (Phys. Rev. Lett. 98, 186803 (2007)) have computed Fock-Darwin levels of a graphene dot by including only basis states with energies larger than or equal to zero. We show that their results violate the Hellman-Feynman theorem. A correct treatment must include both positive and negative energy basis states. Additional basis states lead to new energy levels in the optical spectrum and anticrossings between optical transition lines.Comment: 1 page, 1 figure, accepted for publication in PR

Genetic algorithms with self-organized criticality for dynamic optimization problems

This paper proposes a genetic algorithm (GA) with random immigrants for dynamic optimization problems where the worst individual and its neighbours are replaced every generation. In this GA, the individuals interact with each other and, when their fitness is close, as in the case where the diversity level is low, one single replacement can affect a large number of individuals. This simple approach can take the system to a kind of self-organization behavior, known as self-organized criticality (SOC), which is useful to maintain the diversity of the population in dynamic environments and hence allows the GA to escape from local optima when the problem changes. The experimental results show that the proposed GA presents the phenomenon of SOC

Two-component theory of a droplet of electrons in half-filled Landau level

We have investigated low energy excitations of a disk of electrons in half-filled Landau level using trail wave function and small-size exact diagonalization approaches. We have constructed a set of many-body basis states that describe correctly the low energy excitations. In this theory a droplet consists of two types of composite fermion liquids, and suggests that a droplet can support an edge magnetoplasmon and low energy droplet excitations. A possibility of measuring these excitations in a quantum dot is discussed.Comment: Figure1 is available from the authors upon request. Three eps files are attached to the tex fil

Edge and bulk merons in double quantum dots with spontaneous interlayer phase coherence

We have investigated nucleation of merons in double quantum dots when a lateral distortion with a reflection symmetry is present in the confinement potential. We find that merons can nucleate both inside and at the edge of the dots. In addition to these merons, our results show that electron density modulations can be also present inside the dots. An edge meron appears to have approximately a half integer winding number.Comment: 5 pages, 4 figures, Proceedings of 17th International Conference on High Magnetic Fields in Semiconductor Physic

Self-adaptation of mutation distribution in evolution strategies for dynamic optimization problems

Copyright @ IOS Press. All Rights Reserved.Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on experiments generated from the simulation of evolutionary robots and on dynamic optimization problems generated by the Moving Peaks generator