Sub-structural Niching in Estimation of Distribution Algorithms

We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation of distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The proposed method consists of three key components: (1) Problem decomposition and sub-structure identification, (2) sub-structure fitness estimation, and (3) sub-structural niche preservation. The sub-structural niching method is compared to restricted tournament selection (RTS)--a niching method used in hierarchical Bayesian optimization algorithm--with special emphasis on sustained preservation of multiple global solutions of a class of boundedly-difficult, additively-separable multimodal problems. The results show that sub-structural niching successfully maintains multiple global optima over large number of generations and does so with significantly less population than RTS. Additionally, the market share of each of the niche is much closer to the expected level in sub-structural niching when compared to RTS

An Ultra-High-Density, Transcript-Based, Genetic Map of Lettuce.

We have generated an ultra-high-density genetic map for lettuce, an economically important member of the Compositae, consisting of 12,842 unigenes (13,943 markers) mapped in 3696 genetic bins distributed over nine chromosomal linkage groups. Genomic DNA was hybridized to a custom Affymetrix oligonucleotide array containing 6.4 million features representing 35,628 unigenes of Lactuca spp. Segregation of single-position polymorphisms was analyzed using 213 F7:8 recombinant inbred lines that had been generated by crossing cultivated Lactuca sativa cv. Salinas and L. serriola acc. US96UC23, the wild progenitor species of L. sativa The high level of replication of each allele in the recombinant inbred lines was exploited to identify single-position polymorphisms that were assigned to parental haplotypes. Marker information has been made available using GBrowse to facilitate access to the map. This map has been anchored to the previously published integrated map of lettuce providing candidate genes for multiple phenotypes. The high density of markers achieved in this ultradense map allowed syntenic studies between lettuce and Vitis vinifera as well as other plant species

High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm

We implement a master-slave parallel genetic algorithm (PGA) with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs) to implement a PGA and visualise the results using disjoint minimal spanning trees (MSTs). We demonstrate that our GPU PGA, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable due to compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.Comment: 10 pages, 5 figures, 4 tables, More thorough discussion of implementatio