Substantial changes in the genetic basis of tadpole morphology of Rana lessonae in the presence of predators

Predator-induced morphological plasticity is a model system for investigating phenotypic plasticity in an ecological context. We investigated the genetic basis of the predator-induced plasticity in Rana lessonae by determining the pattern of genetic covariation of three morphological traits that were found to be induced in a predatory environment. Body size decreased and tail dimensions increased when reared in the presence of preying dragonfly larvae. Genetic variance in body size increased by almost an order of magnitude in the predator environment, and the first genetic principal component was found to be highly significantly different between the two environments. The across environment genetic correlation for body size was significantly below 1 indicating that different genes contributed to this trait in the two environments. Body size may therefore be able to respond to selection independently in the two environments to some extent.

The evolutionary history of Symbiodinium and scleractinian hosts - Symbiosis, diversity, and the effect of climate change

Marine invertebrates representing at least five phyla are symbiotic with dinoflagellates from the genus Symbiodinium. This group of single-celled protists was once considered to be a single pandemic species, Symbiodinium microadriaticum. Molecular investigations over the past 25 years have revealed, however, that Symbiodinium is a diverse group of organisms with at least eight (A-H) divergent clades that in turn contain multiple molecular subclade types. The diversity within this genus may subsequently determine the response of corals to normal and stressful conditions, leading to the proposal that the symbiosis may impart unusually rapid adaptation to environmental change by the metazoan host. These questions have added importance due to the critical challenges that corals and the reefs they build face as a consequence of current rapid climate change. This review outlines our current understanding of the diverse genus Symbiodinium and explores the ability of this genus and its symbioses to adapt to rapid environmental change. (c) 2006 Rubel Foundation, ETH Zurich. Published by Elsevier GmbH. All rights reserved.

Genetic structure and male-mediated gene flow in the ghost bat (Macroderma gigas)

The Australian ghost bat is a large, opportunistic carnivorous species that has undergone a marked range contraction toward more mesic, tropical sites over the past century. Comparison of mitochondrial DNA (mtDNA) control region sequences and six nuclear microsatellite loci in 217 ghost bats from nine populations across subtropical and tropical Australia revealed strong population subdivision (mtDNA phi(ST) = 0.80; microsatellites URST = 0.337). Low-latitude (tropical) populations had higher heterozygosity and less marked phylogeographic structure and lower subdivision among sites within regions (within Northern Territory [NT] and within North Queensland [NQ]) than did populations at higher latitudes (subtropical sites; central Queensland [CQ]), although sampling of geographically proximal breeding sites is unavoidably restricted for the latter. Gene flow among populations within each of the northern regions appears to be male biased in that the difference in population subdivision for mtDNA and microsatellites (NT phi(ST) = 0.39, URST = 0.02; NQ phi(ST) = 0.60, URST = -0.03) is greater than expected from differences in the effective population size of haploid versus diploid loci. The high level of population subdivision across the range of the ghost bat contrasts with evidence for high gene flow in other chiropteran species and may be due to narrow physiological tolerances and consequent limited availability of roosts for ghost bats, particularly across the subtropical and relatively arid regions. This observation is consistent with the hypothesis that the contraction of the species' range is associated with late Holocene climate change. The extreme isolation among higher-latitude populations may predispose them to additional local extinctions if the processes responsible for the range contraction continue to operate.

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.

A genetic algorithm based method for deregulated electricity market dispatch

In a deregulated electricity market, optimizing dispatch capacity and transmission capacity are among the core concerns of market operators. Many market operators have capitalized on linear programming (LP) based methods to perform market dispatch operation in order to explore the computational efficiency of LP. In this paper, the search capability of genetic algorithms (GAs) is utilized to solve the market dispatch problem. The GA model is able to solve pool based capacity dispatch, while optimizing the interconnector transmission capacity. Case studies and corresponding analyses are performed to demonstrate the efficiency of the GA model.

Learning to play Pac-Man: An evolutionary, rule-based approach

Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. We describe an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset. with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.

On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

In this paper, we address some issue related to evaluating and testing evolutionary algorithms. A landscape generator based on Gaussian functions is proposed for generating a variety of continuous landscapes as fitness functions. Through some initial experiments, we illustrate the usefulness of this landscape generator in testing evolutionary algorithms.

PCB assembly line assignment:a genetic algorithm approach

Purpose – This paper sets out to study a production-planning problem for printed circuit board (PCB) assembly. A PCB assembly company may have a number of assembly lines for production of several product types in large volume. Design/methodology/approach – Pure integer linear programming models are formulated for assigning the product types to assembly lines, which is the line assignment problem, with the objective of minimizing the total production cost. In this approach, unrealistic assignment, which was suffered by previous researchers, is avoided by incorporating several constraints into the model. In this paper, a genetic algorithm is developed to solve the line assignment problem. Findings – The procedure of the genetic algorithm to the problem and a numerical example for illustrating the models are provided. It is also proved that the algorithm is effective and efficient in dealing with the problem. Originality/value – This paper studies the line assignment problem arising in a PCB manufacturing company in which the production volume is high.

A genetic algorithm for the generalised transportation problem

The generalised transportation problem (GTP) is an extension of the linear Hitchcock transportation problem. However, it does not have the unimodularity property, which means the linear programming solution (like the simplex method) cannot guarantee to be integer. This is a major difference between the GTP and the Hitchcock transportation problem. Although some special algorithms, such as the generalised stepping-stone method, have been developed, but they are based on the linear programming model and the integer solution requirement of the GTP is relaxed. This paper proposes a genetic algorithm (GA) to solve the GTP and a numerical example is presented to show the algorithm and its efficiency.

Evolutionary neural networks : models and applications

The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA discovers bumptrees which significantly out-perform those constructed using a standard algorithm. The fields of artificial life, control and robotics are identified as likely application areas for the evolutionary optimisation of NNs. An artificial life case-study is presented and discussed. Experiments are reported which show that the GA-bumptree is able to learn simulated pole balancing and car parking tasks using only limited environmental feedback. A simple modification of the fitness function allows the GA-bumptree to learn mappings which are multi-modal, such as robot arm inverse kinematics. The dynamics of the 'geographic speciation' selection model used by the GA-bumptree are investigated empirically and the convergence profile is introduced as an analytical tool. The relationships between the rate of genetic convergence and the phenomena of speciation, genetic drift and punctuated equilibrium arc discussed. The importance of genetic linkage to GA design is discussed and two new recombination operators arc introduced. The first, linkage mapped crossover (LMX) is shown to be a generalisation of existing crossover operators. LMX provides a new framework for incorporating prior knowledge into GAs.Its adaptive form, ALMX, is shown to be able to infer linkage relationships automatically during genetic search.

Genetic algorithms for discovery of matrix multiplication methods

We present a parallel genetic algorithm for nding matrix multiplication algo-rithms. For 3 x 3 matrices our genetic algorithm successfully discovered algo-rithms requiring 23 multiplications, which are equivalent to the currently best known human-developed algorithms. We also studied the cases with less mul-tiplications and evaluated the suitability of the methods discovered. Although our evolutionary method did not reach the theoretical lower bound it led to an approximate solution for 22 multiplications.

Solving Travelling Salesman Problem in a Simulation of Genetic Algorithms with DNA

In this paper it is explained how to solve a fully connected N-City travelling salesman problem (TSP) using a genetic algorithm. A crossover operator to use in the simulation of a genetic algorithm (GA) with DNA is presented. The aim of the paper is to follow the path of creating a new computational model based on DNA molecules and genetic operations. This paper solves the problem of exponentially size algorithms in DNA computing by using biological methods and techniques. After individual encoding and fitness evaluation, a protocol of the next step in a GA, crossover, is needed. This paper also shows how to make the GA faster via different populations of possible solutions.