Real-coded genetic algorithm for machining condition optimization

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.

The potential benefits of divergent thinking and metacognitive skills in STEAM learning: A discussion paper

In the wake of an almost decade long economic downturn and increasing competition from developing economies, a new agenda in the Australian Government for science, technology, engineering, and mathematics (STEM) education and research has emerged as a national priority. However, to art and design educators, the pervasiveness and apparent exclusivity of STEM can be viewed as another instance of art and design education being relegated to the margins of curriculum (Greene, 1995). In the spirit of interdisciplinarity, there have been some recent calls to expand STEM education to include the arts and design, transforming STEM into STEAM in education (Maeda, 2013). As with STEM, STEAM education emphasises the connections between previously disparate disciplines, meaning that education has been conceptualised in different ways, such as focusing on the creative design thinking process that is fundamental to engineering and art (Bequette & Bequette, 2012). In this article, we discuss divergent creative design thinking process and metacognitive skills, how, and why they may enhance learning in STEM and STEAM.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

A real coded genetic algorithm for data partitioning and scheduling in networks with arbitrary processor release time

The problem of scheduling divisible loads in distributed computing systems, in presence of processor release time is considered. The objective is to find the optimal sequence of load distribution and the optimal load fractions assigned to each processor in the system such that the processing time of the entire processing load is a minimum. This is a difficult combinatorial optimization problem and hence genetic algorithms approach is presented for its solution.

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems. (C) 2004 Elsevier Ltd. All rights reserved.

Genetic Mechanisms Underlying Autism Spectrum Disorders

Autism is a childhood-onset developmental disorder characterized by deficits in reciprocal social interaction, verbal and non-verbal communication, and dependence on routines and rituals. It belongs to a spectrum of disorders (autism spectrum disorders, ASDs) which share core symptoms but show considerable variation in severity. The whole spectrum affects 0.6-0.7% of children worldwide, inducing a substantial public health burden and causing suffering to the affected families. Despite having a very high heritability, ASDs have shown exceptional genetic heterogeneity, which has complicated the identification of risk variants and left the etiology largely unknown. However, recent studies suggest that rare, family-specific factors contribute significantly to the genetic basis of ASDs. In this study, we investigated the role of DISC1 (Disrupted-in-schizophrenia-1) in ASDs, and identified association with markers and haplotypes previously associated with psychiatric phenotypes. We identified four polymorphic micro-RNA target sites in the 3 UTR of DISC1, and showed that hsa-miR-559 regulates DISC1 expression in vitro in an allele-specific manner. We also analyzed an extended autism pedigree with genealogical roots in Central Finland reaching back to the 17th century. To take advantage of the beneficial characteristics of population isolates to gene mapping and reduced genetic heterogeneity observed in distantly related individuals, we performed a microsatellite-based genome-wide screen for linkage and linkage disequilibrium in this pedigree. We identified a putative autism susceptibility locus on chromosome 19p13.3 and obtained further support for previously reported loci at 1q23 and 15q11-q13. To follow-up these findings, we extended our study sample from the same sub-isolate and initiated a genome-wide analysis of homozygosity and allelic sharing using high-density SNP markers. We identified a small number of haplotypes shared by different subsets of the genealogically connected cases, along with convergent biological pathways from SNP and gene expression data, which highlighted axon guidance molecules in the pathogenesis of ASDs. In conclusion, the results obtained in this thesis show that multiple distinct genetic variants are responsible for the ASD phenotype even within single pedigrees from an isolated population. We suggest that targeted resequencing of the shared haplotypes, linkage regions, and other susceptibility loci is essential to identify the causal variants. We also report a possible micro-RNA mediated regulatory mechanism, which might partially explain the wide-range neurobiological effects of the DISC1 gene.

Data integration from two microarray platforms identifies bi-allelic genetic inactivation of RIC8A in a breast cancer cell line

Background: Using array comparative genomic hybridization (aCGH), a large number of deleted genomic regions have been identified in human cancers. However, subsequent efforts to identify target genes selected for inactivation in these regions have often been challenging. Methods: We integrated here genome-wide copy number data with gene expression data and non-sense mediated mRNA decay rates in breast cancer cell lines to prioritize gene candidates that are likely to be tumour suppressor genes inactivated by bi-allelic genetic events. The candidates were sequenced to identify potential mutations. Results: This integrated genomic approach led to the identification of RIC8A at 11p15 as a putative candidate target gene for the genomic deletion in the ZR-75-1 breast cancer cell line. We identified a truncating mutation in this cell line, leading to loss of expression and rapid decay of the transcript. We screened 127 breast cancers for RIC8A mutations, but did not find any pathogenic mutations. No promoter hypermethylation in these tumours was detected either. However, analysis of gene expression data from breast tumours identified a small group of aggressive tumours that displayed low levels of RIC8A transcripts. qRT-PCR analysis of 38 breast tumours showed a strong association between low RIC8A expression and the presence of TP53 mutations (P = 0.006). Conclusion: We demonstrate a data integration strategy leading to the identification of RIC8A as a gene undergoing a classical double-hit genetic inactivation in a breast cancer cell line, as well as in vivo evidence of loss of RIC8A expression in a subgroup of aggressive TP53 mutant breast cancers.

Using IRAP markers for analysis of genetic variability in populations of resource and rare species of plants

Species specific LTR retrotransposons were first cloned in five rare relic species of drug plants located in the Perm’ region. Sequences of LTR retrotransposons were used for PCR analysis based on amplification of repeated sequences from LTR or other sites of retrotransposons (IRAP). Genetic diversity was studied in six populations of rare relic species of plants Adonis vernalis L. by means of the IRAP method; 125 polymorphic IRAP markers were analyzed. Parameters for DNA polymorphism and genetic diversity of A. vernalis populations were determined.

A Dynamic Migration Model for self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Using IRAP markers for analysis of genetic variability in populations of resource and rare species of plants

Species specific LTR retrotransposons were first cloned in five rare relic species of drug plants located in the Perm’ region. Sequences of LTR retrotransposons were used for PCR analysis based on amplification of repeated sequences from LTR or other sites of retrotransposons (IRAP). Genetic diversity was studied in six populations of rare relic species of plants Adonis vernalis L. by means of the IRAP method; 125 polymorphic IRAP markers were analyzed. Parameters for DNA polymorphism and genetic diversity of A. vernalis populations were determined.

Identification of genetic susceptibility loci for migraine

Migraine is the common cause of chronic episodic headache, affecting 12%-15% of the Caucasian population (41 million Europeans and some half a million Finns), and causes considerable loss of quality of life to its sufferers, as well as being linked to increased risk for a wide range of conditions, from depression to stroke. Migraine is the 19th most severe disease in terms of disability-adjusted life years, and 9th among women. It is characterized by attacks of headache accompanied by sensitivity to external stimuli lasting 4-72 hours, and in a third of cases by neurological aura symptoms, such as loss of vision, speech or muscle function. The underlying pathophysiology, including what triggers migraine attacks and why they occur in the first place, is largely unknown. The aim of this study was to identify genetic factors associated with the hereditary susceptibility to migraine, in order to gain a better understanding of migraine mechanisms. In this thesis, we report the results of genetic linkage and association analyses on a Finnish migraine patient collection as well as migraineurs from Australia, Denmark, Germany, Iceland and the Netherlands. Altogether we studied genetic information of nearly 7,000 migraine patients and over 50,000 population-matched controls. We also developed a new migraine analysis method called the trait component analysis, which is based on individual patient responses instead of the clinical diagnosis. Using this method, we detected a number of new genetic loci for migraine, including on chromosome 17p13 (HLOD 4.65) and 10q22-q23 (female-specific HLOD 7.68) with significant evidence of linkage, along with five other loci (2p12, 8q12, 4q28-q31, 18q12-q22, and Xp22) detected with suggestive evidence of linkage. The 10q22-q23 locus was the first genetic finding in migraine to show linkage to the same locus and markers in multiple populations, with consistent detection in six different scans. Traditionally, ion channels have been thought to play a role in migraine susceptibility, but we were able to exclude any significant role for common variants in a candidate gene study of 155 ion transport genes. This was followed up by the first genome-wide association study in migraine, conducted on 2,748 migraine patients and 10,747 matched controls followed by a replication in 3,209 patients and 40,062 controls. In this study, we found interesting results with genome-wide significance, providing targets for future genetic and functional studies. Overall, we found several promising genetic loci for migraine providing a promising base for future studies in migraine.