Natalio Krasnogor

Recent advances in memetic algorithms

to Memetic Algorithms.- Memetic Evolutionary Algorithms.- Applications of Memetic Algorithms.- An Evolutionary Approach for the Maximum Diversity Problem.- Multimeme Algorithms Using Fuzzy Logic Based Memes For Protein Structure Prediction.- A Memetic Algorithm Solving the VRP, the CARP and General Routing Problems with Nodes, Edges and Arcs.- Using Memetic Algorithms for Optimal Calibration of Automotive Internal Combustion Engines.- The Co-Evolution of Memetic Algorithms for Protein Structure Prediction.- Hybrid Evolutionary Approaches to Terminal Assignment in Communications Networks.- Effective Exploration & Exploitation of the Solution Space via Memetic Algorithms for the Circuit Partition Problem.- Methodological Aspects of Memetic Algorithms.- Towards Robust Memetic Algorithms.- NK-Fitness Landscapes and Memetic Algorithms with Greedy Operators and k-opt Local Search.- Self-Assembling of Local Searchers in Memetic Algorithms.- Designing Efficient Genetic and Evolutionary Algorithm Hybrids.- The Design of Memetic Algorithms for Scheduling and Timetabling Problems.- Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects.- Related Search Technologies.- A Memetic Learning Classifier System for Describing Continuous-Valued Problem Spaces.- Angels & Mortals: A New Combinatorial Optimization Algorithm.

Real-coded memetic algorithms with crossover hill-climbing

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.

EnrichNet: network-based gene set enrichment analysis

Motivation: Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized. Results: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins. Availability: EnrichNet is freely available at http://www.enrichnet.org. Contact: Natalio.Krasnogor@nottingham.ac.uk, reinhard.schneider@uni.lu or avalencia@cnio.es Supplementary Information: Supplementary data are available at Bioinformatics Online.

Nature Inspired Cooperative Strategies for Optimization (NICSO 2011)

Extending the ABC-Miner Bayesian Classification Algorithm.- A Multiple Pheromone Ant Clustering Algorithm.- An Island Memetic Differential Evolution Algorithm for the Feature Selection Problem.- Using a Scouting Predator-Prey Optimizer to Train Support Vector Machines with non PSD Kernels.- Response Surfaces with Discounted Information for Global Optima Tracking in Dynamic Environments.- Fitness based Self Adaptive Differential.- Adaptation schemes and dynamic optimization problems: a basic study on the Adaptive Hill Climbing Memetic Algorithm.- Using base position errors in an entropy-based evaluation function for the study of genetic code adaptability.- An Adaptive Multi-Crossover Population Algorithm for Solving Routing Problems.- Corner Based Many-Objective Optimization.- Escaping Local Optima via Parallelization and.- An Improved Genetic Based Keyword Extraction Technique.- Part-of-Speech Tagging Using Evolutionary Computation.- A Cooperative approach using ants and bees for the graph coloring problem.- Artificial Bee Colony Training of Neural Networks.- Nonlinar optimization in landscapes with planar regions.- Optimizing Neighbourhood Distances for a Variant of Fully-Informed Particle Swarm Algorithm.- Meta Morphic Particle Swarm Optimization.- Empirical study of computational intelligence strategies for biochemical systems modelling.- Metachronal waves in Cellular Automata: Cilia-like manipulation in actuator arrays.- Team of A-Teams Approach for Vehicle Routing Problem with Time Windows.- Self-adaptable Group Formation of Reconfigurable Agents in Dynamic Environments.- A Choice Function Hyper-Heuristic for the Winner Determination Problem.- Automatic Generation of Heuristics for Constraint Satisfaction Problems.- Branching Schemes and Variable Ordering Heuristics for Constraint Satisfaction Problems: Is there Something to Learn.- Nash Equilibria Detection for Discrete-time Generalized Cournot Dynamic Oligopolies.

Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions

Seed germination is a complex trait of key ecological and agronomic significance. Few genetic factors regulating germination have been identified, and the means by which their concerted action controls this developmental process remains largely unknown. Using publicly available gene expression data from Arabidopsis thaliana, we generated a condition-dependent network model of global transcriptional interactions (SeedNet) that shows evidence of evolutionary conservation in flowering plants. The topology of the SeedNet graph reflects the biological process, including two state-dependent sets of interactions associated with dormancy or germination. SeedNet highlights interactions between known regulators of this process and predicts the germination-associated function of uncharacterized hub nodes connected to them with 50% accuracy. An intermediate transition region between the dormancy and germination subdomains is enriched with genes involved in cellular phase transitions. The phase transition regulators SERRATE and EARLY FLOWERING IN SHORT DAYS from this region affect seed germination, indicating that conserved mechanisms control transitions in cell identity in plants. The SeedNet dormancy region is strongly associated with vegetative abiotic stress response genes. These data suggest that seed dormancy, an adaptive trait that arose evolutionarily late, evolved by coopting existing genetic pathways regulating cellular phase transition and abiotic stress. SeedNet is available as a community resource (http://vseed.nottingham.ac.uk) to aid dissection of this complex trait and gene function in diverse processes.

A thermodynamic switch modulates abscisic acid receptor sensitivity

Abscisic acid (ABA) is a key hormone regulating plant growth, development and the response to biotic and abiotic stress. ABA binding to pyrabactin resistance (PYR)/PYR1‐like (PYL)/Regulatory Component of Abscisic acid Receptor (RCAR) intracellular receptors promotes the formation of stable complexes with certain protein phosphatases type 2C (PP2Cs), leading to the activation of ABA signalling. The PYR/PYL/RCAR family contains 14 genes in Arabidopsis and is currently the largest plant hormone receptor family known; however, it is unclear what functional differentiation exists among receptors. Here, we identify two distinct classes of receptors, dimeric and monomeric, with different intrinsic affinities for ABA and whose differential properties are determined by the oligomeric state of their apo forms. Moreover, we find a residue in PYR1, H60, that is variable between family members and plays a key role in determining oligomeric state. In silico modelling of the ABA activation pathway reveals that monomeric receptors have a competitive advantage for binding to ABA and PP2Cs. This work illustrates how receptor oligomerization can modulate hormonal responses and more generally, the sensitivity of a ligand‐dependent signalling system.

Transcriptional Dynamics of Two Seed Compartments with Opposing Roles in Arabidopsis Seed Germination

Gene expression profiling in two seed compartments uncovers two transcriptional phases during seed germination that are separated by testa rupture. Seed germination is a critical stage in the plant life cycle and the first step toward successful plant establishment. Therefore, understanding germination is of important ecological and agronomical relevance. Previous research revealed that different seed compartments (testa, endosperm, and embryo) control germination, but little is known about the underlying spatial and temporal transcriptome changes that lead to seed germination. We analyzed genome-wide expression in germinating Arabidopsis (Arabidopsis thaliana) seeds with both temporal and spatial detail and provide Web-accessible visualizations of the data reported (vseed.nottingham.ac.uk). We show the potential of this high-resolution data set for the construction of meaningful coexpression networks, which provide insight into the genetic control of germination. The data set reveals two transcriptional phases during germination that are separated by testa rupture. The first phase is marked by large transcriptome changes as the seed switches from a dry, quiescent state to a hydrated and active state. At the end of this first transcriptional phase, the number of differentially expressed genes between consecutive time points drops. This increases again at testa rupture, the start of the second transcriptional phase. Transcriptome data indicate a role for mechano-induced signaling at this stage and subsequently highlight the fates of the endosperm and radicle: senescence and growth, respectively. Finally, using a phylotranscriptomic approach, we show that expression levels of evolutionarily young genes drop during the first transcriptional phase and increase during the second phase. Evolutionarily old genes show an opposite pattern, suggesting a more conserved transcriptome prior to the completion of germination.

Memetic Evolutionary Algorithms

Memetic Evolutionary Algorithms (MAs) are a class of stochastic heuristics for global optimization which combine the parallel global search nature of Evolutionary Algorithms with Local Search to improve individual solutions. These techniques are being applied to an increasing range of application domains with successful results, and the aim of this book is both to highlight some of these applications, and to shed light on some of the design issues and considerations necessary to a successful implementation. In this chapter we provide a background for the rest of the volume by introducing Evolutionary Algorithms (EAs) and Local Search. We then move on to describe the synergy that arises when these two are combined in Memetic Algorithms, and to discuss some of the most salient design issues for a successful implementation. We conclude by describing various other ways in which EAs and MAs can be hybridized with domain-specific knowledge and other search techniques.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

BackgroundStatistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks.ResultsWe present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining.ConclusionArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

The imitation game - A computational chemical approach to recognizing life

When is an artificial cell alive? A Turing test–like method may provide the answer.