Improved joint control using a genetic algorithm for a humanoid robot

This paper describes experiments conducted in order to simultaneously tune 15 joints of a humanoid robot. Two Genetic Algorithm (GA) based tuning methods were developed and compared against a hand-tuned solution. The system was tuned in order to minimise tracking error while at the same time achieve smooth joint motion. Joint smoothness is crucial for the accurate calculation of online ZMP estimation, a prerequisite for a closedloop dynamically stable humanoid walking gait. Results in both simulation and on a real robot are presented, demonstrating the superior smoothness performance of the GA based methods.

Strategies for identifying and predicting islet autoantigen T-cell epitopes in insulin-dependent diabetes mellitus

T cells recognize peptide epitopes bound to major histocompatibility complex molecules. Human T-cell epitopes have diagnostic and therapeutic applications in autoimmune diseases. However, their accurate definition within an autoantigen by T-cell bioassay, usually proliferation, involves many costly peptides and a large amount of blood, We have therefore developed a strategy to predict T-cell epitopes and applied it to tyrosine phosphatase IA-2, an autoantigen in IDDM, and HLA-DR4(*0401). First, the binding of synthetic overlapping peptides encompassing IA-2 was measured directly to purified DR4. Secondly, a large amount of HLA-DR4 binding data were analysed by alignment using a genetic algorithm and were used to train an artificial neural network to predict the affinity of binding. This bioinformatic prediction method was then validated experimentally and used to predict DR4 binding peptides in IA-2. The binding set encompassed 85% of experimentally determined T-cell epitopes. Both the experimental and bioinformatic methods had high negative predictive values, 92% and 95%, indicating that this strategy of combining experimental results with computer modelling should lead to a significant reduction in the amount of blood and the number of peptides required to define T-cell epitopes in humans.

Knowledge-based system on optimum design of liquid retaining structures with genetic algorithms

This paper delineates the development of a prototype hybrid knowledge-based system for the optimum design of liquid retaining structures by coupling the blackboard architecture, an expert system shell VISUAL RULE STUDIO and genetic algorithm (GA). Through custom-built interactive graphical user interfaces under a user-friendly environment, the user is directed throughout the design process, which includes preliminary design, load specification, model generation, finite element analysis, code compliance checking, and member sizing optimization. For structural optimization, GA is applied to the minimum cost design of structural systems with discrete reinforced concrete sections. The design of a typical example of the liquid retaining structure is illustrated. The results demonstrate extraordinarily converging speed as near-optimal solutions are acquired after merely exploration of a small portion of the search space. This system can act as a consultant to assist novice designers in the design of liquid retaining structures.

Genetic algorithms for design of liquid retaining structure

In this paper, genetic algorithm (GA) is applied to the optimum design of reinforced concrete liquid retaining structures, which comprise three discrete design variables, including slab thickness, reinforcement diameter and reinforcement spacing. GA, being a search technique based on the mechanics of natural genetics, couples a Darwinian survival-of-the-fittest principle with a random yet structured information exchange amongst a population of artificial chromosomes. As a first step, a penalty-based strategy is entailed to transform the constrained design problem into an unconstrained problem, which is appropriate for GA application. A numerical example is then used to demonstrate strength and capability of the GA in this domain problem. It is shown that, only after the exploration of a minute portion of the search space, near-optimal solutions are obtained at an extremely converging speed. The method can be extended to application of even more complex optimization problems in other domains.

The GP problem: quantifying gene-to-phenotype relationships

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

Genetic algorithms in statistical tolerancing

An important aspect in manufacturing design is the distribution of geometrical tolerances so that an assembly functions with given probability, while minimising the manufacturing cost. This requires a complex search over a multidimensional domain, much of which leads to infeasible solutions and which can have many local minima. As well, Monte-Carlo methods are often required to determine the probability that the assembly functions as designed. This paper describes a genetic algorithm for carrying out this search and successfully applies it to two specific mechanical designs, enabling comparisons of a new statistical tolerancing design method with existing methods. (C) 2003 Elsevier Ltd. All rights reserved.

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.

Genes for the majority of group A streptococcal virulence factors and extracellular surface proteins do not confer an increased propensity to cause invasive disease

Background. The factors behind the reemergence of severe, invasive group A streptococcal (GAS) diseases are unclear, but it could be caused by altered genetic endowment in these organisms. However, data from previous studies assessing the association between single genetic factors and invasive disease are often conflicting, suggesting that other, as-yet unidentified factors are necessary for the development of this class of disease. Methods. In this study, we used a targeted GAS virulence microarray containing 226 GAS genes to determine the virulence gene repertoires of 68 GAS isolates (42 associated with invasive disease and 28 associated with noninvasive disease) collected in a defined geographic location during a contiguous time period. We then employed 3 advanced machine learning methods (genetic algorithm neural network, support vector machines, and classification trees) to identify genes with an increased association with invasive disease. Results. Virulence gene profiles of individual GAS isolates varied extensively among these geographically and temporally related strains. Using genetic algorithm neural network analysis, we identified 3 genes with a marginal overrepresentation in invasive disease isolates. Significantly, 2 of these genes, ssa and mf4, encoded superantigens but were only present in a restricted set of GAS M-types. The third gene, spa, was found in variable distributions in all M-types in the study. Conclusions. Our comprehensive analysis of GAS virulence profiles provides strong evidence for the incongruent relationships among any of the 226 genes represented on the array and the overall propensity of GAS to cause invasive disease, underscoring the pathogenic complexity of these diseases, as well as the importance of multiple bacteria and/ or host factors.

AUSLEM (AUStralian Land Erodibility Model): A tool for identifying wind erosion hazard in Australia

We present AUSLEM (AUStralian Land Erodibility Model), a land erodibility modelling system that utilizes a rule-set of surficial and climatic thresholds applied through a Geographic Information System (GIs) modelling framework to predict landscape susceptibility to wind erosion. AUSLEM is distinctive in that it quantitatively assesses landscape susceptibility to wind erosion at a 5 x 5 km. spatial resolution on a monthly time-step across Australia. The system was implemented for representative wet (1984), dry (1994), and average rainfall (1997) years with corresponding low, high and moderate dust storm day frequencies. Results demonstrate that AUSLEM can identify landscape erodibility, and provide an interpretation of the physical nature and distribution of erodible landscapes in Australia. Further, results offer an assessment of the dynamic tendencies of erodibility in space and time in response to the El Nino Southern Oscillation (ENSO) and seasonal synoptic scale climate variability. A comparative analysis of AUSLEM output with independent national and international wind erosion, atmospheric aerosol and dust event records indicates a high level of model competency. (c) 2006 Elsevier B.V. All rights reserved.

An implementation of genetic algorithms as a basis for a trading system on the foreign exchange market

Foreign exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process is very helpful. In this paper, we try to create such a system with a genetic algorithm engine to emulate trader behaviour on the foreign exchange market and to find the most profitable trading strategy.

Optimal design of a regenerative dynamic dynamometer using genetic algorithms

This paper presents an approach for optimal design of a fully regenerative dynamic dynamometer using genetic algorithms. The proposed dynamometer system includes an energy storage mechanism to adaptively absorb the energy variations following the dynamometer transients. This allows the minimum power electronics requirement at the mains power supply grid to compensate for the losses. The overall dynamometer system is a dynamic complex system and design of the system is a multi-objective problem, which requires advanced optimisation techniques such as genetic algorithms. The case study of designing and simulation of the dynamometer system indicates that the genetic algorithm based approach is able to locate a best available solution in view of system performance and computational costs.

Investment prioritization based on broadscale spatial budgeting to meet downstream targets for suspended sediment loads

On the basis of a spatially distributed sediment budget across a large basin, costs of achieving certain sediment reduction targets in rivers were estimated. A range of investment prioritization scenarios were tested to identify the most cost-effective strategy to control suspended sediment loads. The scenarios were based on successively introducing more information from the sediment budget. The relationship between spatial heterogeneity of contributing sediment sources on cost effectiveness of prioritization was investigated. Cost effectiveness was shown to increase with sequential introduction of sediment budget terms. The solution which most decreased cost was achieved by including spatial information linking sediment sources to the downstream target location. This solution produced cost curves similar to those derived using a genetic algorithm formulation. Appropriate investment prioritization can offer large cost savings because the magnitude of the costs can vary by several times depending on what type of erosion source or sediment delivery mechanism is targeted. Target settings which only consider the erosion source rates can potentially result in spending more money than random management intervention for achieving downstream targets. Coherent spatial patterns of contributing sediment emerge from the budget model and its many inputs. The heterogeneity in these patterns can be summarized in a succinct form. This summary was shown to be consistent with the cost difference between local and regional prioritization for three of four test catchments. To explain the effect for the fourth catchment, the detail of the individual sediment sources needed to be taken into account.

Robust parameter settings of evolutionary algorithms for the optimisation of agricultural systems models

Numerical optimisation methods are being more commonly applied to agricultural systems models, to identify the most profitable management strategies. The available optimisation algorithms are reviewed and compared, with literature and our studies identifying evolutionary algorithms (including genetic algorithms) as superior in this regard to simulated annealing, tabu search, hill-climbing, and direct-search methods. Results of a complex beef property optimisation, using a real-value genetic algorithm, are presented. The relative contributions of the range of operational options and parameters of this method are discussed, and general recommendations listed to assist practitioners applying evolutionary algorithms to the solution of agricultural systems. (C) 2001 Elsevier Science Ltd. All rights reserved.

Is maximizing resilience compatible with established ecological goal functions?

Cropp and Gabric [Ecosystem adaptation: do ecosystems maximise resilience? Ecology. In press] used a simple phytoplanktonzooplankton-nutrient model and a genetic algorithm to determine the parameter values that would maximize the value of certain goal functions. These goal functions were to maximize biomass, maximize flux, maximize flux to biomass ratio, and maximize resilience. It was found that maximizing goal functions maximized resilience. The objective of this study was to investigate whether the Cropp and Gabric [Ecosystem adaptation: do ecosystems maximise resilience? Ecology. In press] result was indicative of a general ecosystem principle, or peculiar to the model and parameter ranges used. This study successfully replicated the Cropp and Gabric [Ecosystem adaptation: do ecosystems maximise resilience? Ecology. In press] experiment for a number of different model types, however, a different interpretation of the results is made. A new metric, concordance, was devised to describe the agreement between goal functions. It was found that resilience has the highest concordance of all goal functions trialled. for most model types. This implies that resilience offers a compromise between the established ecological goal functions. The parameter value range used is found to affect the parameter versus goal function relationships. Local maxima and minima affected the relationship between parameters and goal functions, and between goal functions. (C) 2003 Elsevier B.V. All rights reserved.