bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Spiritual and Religious Activity on Quality of Sleep in Hemodialysis Patients
Authors: Yang Ju-Yeh , Huang Jenq-Wen , Kao Tze-Wah , Peng Yu-Sen , Lu Chia-Sheng , Chen Da-Lung , Yang Chwei-Shiun , Yang Chih-Ching , Tsai Dong-Ming , Liao Cheng-Shiung , Chang Hong-Wei , Wu Wen-Chung , Wu Ming-Shiou , Wu Kwan-Dun , Chang Chee-Jen , Tsai Tun-Jun , Chen Wan-Yu
Published: February 2008

Background: Sleep disorders are common in hemodialysis (HD) patients. This study examined the relationship between quality of sleep (QoS) and religious/spiritual activity in HD patients. Methods: The study subjects were 861 HD patients from 14 dialysis clinics in Taiwan. QoS and religious/spiritual activity were evaluated by the Pittsburgh Sleep Quality Index (PSQI) questionnaire and the Royal Free Questionnaire respectively. Results: There was no difference in clinical parameters between the good and poor sleepers. Although total scores of religious and spiritual activity did not correlate with global PSQI score, patients who held strong ‘spiritual’ beliefs reported more problems in ‘sleep disturbances’, while those who exercised religious beliefs more strongly reported less trouble in ‘daytime dysfunction’. Conclusion: There is no significant correlation between QoS and religious/spiritual activity globally. However, the spiritual and religious activity did associate with different components of QoS.
Smoking-Related Changes in Cerebral Perfusion in a Population of Elderly Men
Authors: Siennicki-Lantz Arkadiusz , Reinprecht Faina , Wollmer Per , Elmståhl Sölve
Published: February 2008

Aim: Smoking and hypertension are known causes of stroke and dementia. This study was designed to test the longitudinal effect of lifestyle factors and hypertension on cerebral blood flow (CBF) in old age. Method: A study of an unselected population cohort of 703 men, born in 1914, has been in progress since 1968 and the last clinical examination occurred at age 69. CBF was examined in 129 survivors who reached 82 years, using SPECT. Results: At the age of 69, the study subjects consisted of 36 smokers, 37 nonsmokers and 56 former smokers who had quit. Fourteen years later, there were no differences in CBF among them. The cohort was stratified into 69 hypertensive and 60 normotensive men. CBF in normotensives was lowest in smokers and highest in nonsmokers. No CBF differences were observed in hypertensive men regarding smoking, but their CBF was as low as in normotensive smokers. Hypertensives had a higher BMI, and higher blood glucose and triglyceride levels, but lower alcohol consumption at 69 and a lower ankle-brachial pressure index at 82 years. Alcohol consumption was highest in normotensive smokers. Conclusion: Smoking and high alcohol consumption in normotensive men are correlated to low CBF in senescence. Low CBF in hypertensive men regardless of smoking may be due to a high vascular risk profile.
Antiproliferative Effects of GnRH Agonists: Prospects and Problems for Cancer Therapy
Authors: White Colin D. , Stewart Alan J. , Lu Zhi-Liang , Millar Robert P. , Morgan Kevin
Published: February 2008

Gonadotropin-releasing hormone (GnRH) receptor activation has been demonstrated to inhibit cell proliferation in vitro and in vivo. These effects are dependent on the degree of receptor expression and the intracellular signaling protein milieu. The physiological and pathophysiological relevance is largely undefined, and its potential for exploitation in the treatment of specific malignancies is the subject of ongoing investigations. GnRH receptors are expressed in embryonic, juvenile and adult tissues, including brain, pituitary, gonads, accessory reproductive organs and placenta. The levels of receptor expression vary, from high in pituitary gonadotropes to low in peripheral tissues, although quantification of functional receptor protein has been determined in relatively few cell types. Roles for GnRH receptor signaling at different stages of animal development and its influence on reproductive health remain largely unexplored, except in cases of hereditary hypogonadal infertility. In addition to regulating hormone secretion, GnRH is postulated to act as a chemokine or a growth- and differentiation-inducing factor. Hence, receptor activation may influence the function of neuronal networks in the brain and the maturation of reproductive tissue epithelia. GnRH may also potentially influence the biology of cancerous cells in reproductive tissue since receptor activation may signal terminal differentiation, cell cycle arrest or apoptosis. In this context, the cell surface expression of GnRH receptor is important since it influences the intensity of intracellular signaling, and correlates with the ability to inhibit proliferation in transformed cells in vitro. Here, we review data on the effects of GnRH agonists on cell proliferation and apoptosis, and put forward hypotheses for investigation to determine whether the GnRH receptor acts as a tumor suppressor in neuroendocrine or epithelial cells.
The Vibrio cholerae Mrp System: Cation/Proton Antiport Properties and Enhancement of Bile Salt Resistance in a Heterologous Host
Authors: Dzioba-Winogrodzki Judith , Winogrodzki Olga , Krulwich Terry A. , Boin Markus A. , Dibrov Pavel
Published: February 2008

The mrp operon from Vibrio cholerae encoding a putative multisubunit Na+/H+ antiporter was cloned and functionally expressed in the antiporter-deficient strain of Escherichia coli EP432. Cells of EP432 expressing Vc-Mrp exhibited resistance to Na+ and Li+ as well as to natural bile salts such as sodium cholate and taurocholate. When assayed in everted membrane vesicles of the E. coli EP432 host, Vc-Mrp had sufficiently high antiport activity to facilitate the first extensive analysis of Mrp system from a Gram-negative bacterium encoded by a group 2 mrp operon. Vc-Mrp was found to exchange protons for Li+, Na+, and K+ ions in pH-dependent manner with maximal activity at pH 9.0–9.5. Exchange was electrogenic (more than one H+ translocated per cation moved in opposite direction). The apparent Km at pH 9.0 was 1.08, 1.30, and 68.5 mM for Li+, Na+, and K+, respectively. Kinetic analyses suggested that Vc-Mrp operates in a binding exchange mode with all cations and protons competing for binding to the antiporter. The robust ion antiport activity of Vc-Mrp in sub-bacterial vesicles and its effect on bile resistance of the heterologous host make Vc-Mrp an attractive experimental model for the further studies of biochemistry and physiology of Mrp systems.
X-Linked Inhibitor of Apoptosis Protein Is an Important Regulator of Vascular Endothelial Growth Factor–Dependent Bovine Aortic Endothelial Cell Survival
Authors: Kim Jongmin , Park Jongbong , Choi Seungmin , Chi Sung-Gil , Mowbray Amy L. , Jo Hanjoong , Park Heonyong
Published: February 2008

Vascular endothelial growth factor (VEGF) is a critical regulator of endothelial cell biology and vascular function. Chronic VEGF treatment has been shown to inhibit tumor necrosis factor–induced apoptosis in endothelial cells. However, the mechanism for this cell survival is unclear. Interestingly, VEGF also enhances the expression of X-linked inhibitor of apoptosis (XIAP), a well-established antiapoptotic factor. XIAP has been shown to suppress apoptosis by blocking caspase activity in cancer cells, but it remains under studied in the endothelium. Therefore, we hypothesized that VEGF affects important endothelial functions, such as apoptosis and cell migration, by regulating XIAP expression and downstream caspase activity. To test this hypothesis, caspase activity, apoptosis, and cell migration were assessed following XIAP overexpression or depletion in bovine aortic endothelial cells. Much like VEGF treatment, ectopic expression of XIAP blocked tumor necrosis factor–induced apoptosis. Surprisingly, the mechanism was caspase-independent. In addition, XIAP-associated cell survival was the result of enhanced nitric oxide (NO) production, and XIAP was partially localized in caveolae. In these lipid rafts, XIAP interacted with a regulator of NO production, caveolin-1, via a binding motif (
F
t
F
gtwi
Y
, where the bold letters represent aromatic amino acids) in the baculoviral IAP repeat-3 domain. Endothelial NO synthase binding to caveolin-1 was competitively inhibited by XIAP, suggesting that XIAP acts as a modulator of NO production by releasing endothelial NO synthase from caveolin-1. Further studies showed that endothelial cell migration was also controlled by XIAP-dependent NO. Taken together, these results suggest that XIAP plays a novel role in endothelial cells, interacting with caveolin-1 and acting as a regulator of vascular antiatherogenic function.
Variation in the Estrogen Receptor α Gene and Risk of Stroke
Authors: Bos Michiel J. , Schuit Stephanie C.E. , Koudstaal Peter J. , Hofman Albert , Uitterlinden André G. , Breteler Monique M.B.
Published: February 2008

Background and Purpose—

Variations in the −397T>C (rs2234693) and −351A>G (rs9340799) single nucleotide polymorphisms of the estrogen α receptor (
ESR1
) gene were found to be strongly associated with risk of ischemic heart disease, although not all studies could replicate this finding. One study also reported an association with stroke. We assessed whether variations in the
ESR1
gene are associated with the risk of stroke in the general population.

Methods—

This prospective population-based study was based on 6229 Rotterdam Study participants who at baseline (1990–1993) were aged 55 years or older, free from stroke, and had assessment of the
ESR1
rs2234693 and rs9340799 single nucleotide polymorphisms. Follow-up for incident stroke was complete until January 1, 2005. Data were analyzed with Cox proportional hazards models for men and women separately with adjustment for age.

Results—

During an average follow-up time of 10.1 years, 659 strokes occurred, of which 386 were ischemic. Three common haplotypes were identified: −397T/−351A (carried by 78% of all participants), −397C/−351G (carried by 57%), and −397C/−351A (carried by 22%). Although we had at least 89% power to detect a relative risk of 1.5 (α=0.05) in all subgroups, we did not find any association between
ESR1
haplotype carriership and risk of stroke and ischemic stroke.

Conclusions—

We have not been able to replicate the previously reported association between variations in the
ESR1
gene and risk of stroke.
Effects of Prophylactic Antibiotic Therapy With Mezlocillin Plus Sulbactam on the Incidence and Height of Fever After Severe Acute Ischemic Stroke
Authors: Schwarz Stefan , Al-Shajlawi Frank , Sick Christian , Meairs Stephen , Hennerici Michael G.
Published: February 2008

Background and Purpose—

Fever after stroke is a strong predictor for a negative outcome with infections as the most common cause. The aim of this pilot study was to evaluate the effects of prophylactic antibiotic therapy on the incidence and height of fever after acute ischemic stroke.

Methods—

This is a randomized, controlled study of antibiotic prophylaxis in patients with ischemic stroke enrolled within 24 hours from clinical onset who presented bedridden (modified Rankin score >3) with no significant infection. Interventions included prophylactic mezlocillin plus sulbactam (3×2 g/1 g for 4 days) or conventional management. Over 10 days, body temperature was continuously monitored, and the presence of infection was daily assessed. Primary end points were incidence and height of fever; secondary end points included rate of infection and clinical outcome.

Results—

Sixty patients were included (mean, 75 years; median National Institutes of Health Stroke Scale score, 16). Over the first 3 days, patients in the intervention group showed lower mean body temperatures as well as lower daily peak temperatures (
P
<0.05). Throughout the observation period, 15 of 30 patients in the intervention group but 27 of 30 patients in the conventionally treated group developed an infection (
P
<0.05). Mean interval until the diagnosis of infection was 5.1 days in the intervention group and 3.3 days in the control group (
P
<0.05). Clinical outcome was more favorable in patients with prophylactic therapy (
P
=0.01).

Conclusions—

In patients with acute severe stroke, prophylactic administration of mezlocillin plus sulbactam over 4 days decreases body temperature, lowers the rate of infection, and may be associated with a better clinical outcome.
Development of a Metropolitan Matrix of Primary Stroke Centers
Authors: Demaerschalk Bart M. , Bobrow Bentley J. , Paulsen Mary
Published: February 2008

Background and Purpose—

In 1998, 2947 patients in metropolitan Phoenix were hospitalized for acute cerebral infarction. Only 2 of the 26 regional hospitals satisfied criteria for primary stroke center (PSC) designation. Fewer than 1% of patients with ischemic stroke received tissue plasminogen activator for thrombolysis. We sought to develop and evaluate the effectiveness of a metropolitan prehospital emergency medical system for effectively identifying and transporting patients with acute stroke to a matrix of predesignated PSCs and increasing to 20% the proportion of all such patients receiving tissue plasminogen activator.

Methods—

The American Stroke Association Phoenix Operation Stroke partnered with the Arizona Emergency Medical Systems in 1998 to 1999 to list goals and objectives, identify key stakeholders, and develop committees to address community education, emergency medical system training, and PSC designation.

Results—

Over 8 years, emergency medical system personnel were trained to identify and transport patients with acute stroke to PSCs, 8 hospitals met PSC criteria, the metropolitan matrix of PSCs became operational (in 2003), and 18% of patients with acute ischemic stroke received thrombolysis.

Conclusions—

It is feasible to develop and operationalize a successful metropolitan-wide matrix of PSCs to accommodate emergency medical system-identified and transported patients with acute stroke in a 9000-square-mile region with a population of 3.5 million people.
Searching for Diverse, Cooperative Populations with Genetic Algorithms
Authors: Smith Robert E. , Forrest Stephanie , Perelson Alan S.
Published: February 2008

In typical applications, genetic algorithms (GAs) process populations of potential problem solutions to evolve a single population member that specifies an ‘optimized’ solution. The majority of GA analysis has focused on these optimization applications. In other applications (notably learning classifier systems and certain connectionist learning systems), a GA searches for a population of cooperative structures that jointly perform a computational task. This paper presents an analysis of this type of GA problem. The analysis considers a simplified genetics-based machine learning system: a model of an immune system. In this model, a GA must discover a set of pattern-matching antibodies that effectively match a set of antigen patterns. Analysis shows how a GA can automatically evolve and sustain a diverse, cooperative population. The cooperation emerges as a natural part of the antigen-antibody matching procedure. This emergent effect is shown to be similar to fitness sharing, an explicit technique for multimodal GA optimization. Further analysis shows how the GA population can adapt to express various degrees of generalization. The results show how GAs can automatically and simultaneously discover effective groups of cooperative computational structures.
Genetic and Non-Genetic Operators in ALECSYS
Authors: Dorigo Marco
Published: February 2008

It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficulty in regulating interplay between the reward system and the background genetic algorithm (GA), rule chains' instability, default hierarchies' instability, among others. ALECSYS is a parallel version of a standard learning classifier system (CS) and, as such, suffers from these same problems. In this paper we propose some innovative solutions to some of these problems. We introduce the following original features. Mutespec is a new genetic operator used to specialize potentially useful classifiers. Energy is a quantity introduced to measure global convergence to apply the genetic algorithm only when the system is close to a steady state. Dynamic adjustment of the classifiers set cardinality speeds up the performance phase of the algorithm. We present simulation results of experiments run in a simulated two-dimensional world in which a simple agent learns to follow a light source.
Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect
Authors: Gruau Frédéric , Whitley Darrell
Published: February 2008

A grammar tree is used to encode a cellular developmental process that can generate whole families of Boolean neural networks for computing parity and symmetry. The development process resembles biological cell division. A genetic algorithm is used to find a grammar tree that yields both architecture and weights specifying a particular neural network for solving specific Boolean functions. The current study particularly focuses on the addition of learning to the development process and the evolution of grammar trees. Three ways of adding learning to the development process are explored. Two of these exploit the Baldwin effect by changing the fitness landscape without using Lamarckian evolution. The third strategy is Lamarckian in nature. Results for these three modes of combining learning with genetic search are compared against genetic search without learning. Our results suggest that merely using learning to change the fitness landscape can be as effective as Lamarckian strategies at improving search.
A Markov Chain Framework for the Simple Genetic Algorithm
Authors: Davis Thomas E. , Principe Jose C.
Published: February 2008

This paper develops a theoretical framework for the simple genetic algorithm (combinations of the reproduction, mutation, and crossover operators) based on the asymptotic state behavior of a nonstationary Markov chain algorithm model. The methodology borrows heavily from that of simulated annealing. We prove the existence of a unique asymptotic probability distribution (stationary distribution) for the Markov chain when the mutation probability is used with any constant nonzero probability value. We develop a Cramer's Rule representation of the stationary distribution components for all nonzero mutation probability values and then extend the representation to show that the stationary distribution possesses a zero mutation probability limit. Finally, we present a strong ergodicity bound on the mutation probability sequence that ensures that the nonstationary algorithm (which results from varying mutation probability during algorithm execution) achieves the limit distribution asymptotically.

Although the focus of this work is on a nonstationary algorithm in which mutation probability is reduced asymptotically to zero via a schedule (in a fashion analogous to simulated annealing), the stationary distribution results (existence, Cramer's Rule representation, and zero mutation probability limit) are directly applicable to conventional, simple genetic algorithm implementations as well.
Is a Learning Classifier System a Type of Neural Network?
Authors: Smith Robert E. , Cribbs H. Brown
Published: February 2008

This paper suggests a simple analogy between learning classifier systems (LCSs) and neural networks (NNs). By clarifying the relationship between LCSs and NNs, the paper indicates how techniques from one can be utilized in the other. The paper points out that the primary distinguishing characteristic of the LCS is its use of a co-adaptive genetic algorithm (GA), where the end product of evolution is a diverse population of individuals that cooperate to perform useful computation. This stands in contrast to typical GA/NN schemes, where a population of networks is employed to evolve a single, optimized network. To fully illustrate the LCS/NN analogy used in this paper, an LCS-like NN is implemented and tested. The test is constructed to run parallel to a similar GA/NN study that did not employ a co-adaptive GA. The test illustrates the LCS/NN analogy and suggests an interesting new method for applying GAs in NNs. Final comments discuss extensions of this work and suggest how LCS and NN studies can further benefit each other.
A New Representation and Operators for Genetic Algorithms Applied to Grouping Problems
Authors: Falkenauer Emanuel
Published: February 2008

An important class of computational problems are grouping problems, where the aim is to group together members of a set (i.e., find a good partition of the set). We show why both the standard and the ordering GAs fare poorly in this domain by pointing out their inherent difficulty to capture the regularities of the functional landscape of the grouping problems. We then propose a new encoding scheme and genetic operators adapted to these problems, yielding the Grouping Genetic Algorithm (GGA). We give an experimental comparison of the GGA with the other GAs applied to grouping problems, and we illustrate the approach with two more examples of important grouping problems successfully treated with the GGA: the problems of Bin Packing and Economies of Scale.
Genetic Algorithms for Soft-Decision Decoding of Linear Block Codes
Authors: Maini Harpal , Mehrotra Kishan , Mohan Chilukuri , Ranka Sanjay
Published: February 2008

Soft-decision decoding is an NP-hard problem of great interest to developers of communication systems. We show that this problem is equivalent to the problem of optimizing Walsh polynomials. We present genetic algorithms for soft-decision decoding of binary linear block codes and compare the performance with various other decoding algorithms including the currently developed A* algorithm. Simulation results show that our algorithms achieve bit-error-probabilities as low as 0.00183 for a [104,52] code with a low signal-to-noise ratio of 2.5 dB, exploring only 22,400 codewords, whereas the search space contains 4.5 × 10l5 codewords. We define a new crossover operator that exploits domain-specific information and compare it with uniform and two-point crossover.
Memory Exploitation in Learning Classifier Systems
Authors: Smith Robert E.
Published: February 2008

Learning classifier systms (LCSs) offer a unique opportunity to study the adaptive exploitation of memory. Because memory is manipulated in the form of simple internal messages in the LCS, one can easily and carefully examine the development of a system of internal memory symbols. This study examines the LCS applied to a problem whose only performance goal is the effective exploitation of memory. Experimental results show that the genetic algorithm forms a relatively effective set of internal memory symbols, but that this effectiveness is directly limited by the emergence of parasite rules. The results indicate that the emergence of parasites may be an inevitable consequence in a system that must evolve its own set of internal memory symbols. The paper's primary conclusion is that the emergence of parasites is a fundamental obstacle in such problems. To overcome this obstacle, it is suggested that the LCS must form larger, multirule structures. In such structures, parasites can be more accurately evaluated and thus eliminated. This effect is demonstrated through a preliminary evaluation of a classifier corporation scheme. Final comments present future directions for research on memory exploitation in the LCS and similar evolutionary computing systems.
Multiniche Crowding in Genetic Algorithms and Its Application to the Assembly of DNA Restriction-Fragments
Authors: Cedeño Walter , Vemuri V. Rao , Slezak Tom
Published: February 2008

The determination of the sequence of all nucleotide base-pairs in a DNA molecule, from restriction-fragment data, is a complex task and can be posed as the problem of finding the optima of a multimodal function. A genetic algorithm that uses multiniche crowding permits us to do this. Performance of this algorithm is first tested using a standard suite of test functions. The algorithm is next tested using two data sets obtained from the Human Genome Project at the Lawrence Livermore National Laboratory. The new method holds promise in automating the sequencing computations.
Simple Genetic Algorithms with Linear Fitness
Authors: Vose Michael D. , Wright Alden H.
Published: February 2008

A general form of stochastic search is described (random heuristic search), and some of its general properties are proved. This provides a framework in which the simple genetic algorithm (SGA) is a special case. The framework is used to illuminate relationships between seemingly different probabilistic perspectives of SGA behavior. Next, the SGA is formalized as an instance of random heuristic search. The formalization then used to show expected population fitness is a Lyapunov function in the infinite population model when mutation is zero and fitness is linear. In particular, the infinite population algorithm must converge, and average population fitness increases from one generation to the next. The consequence for a finite population SGA is that the expected population fitness increases from one generation to the next. Moreover, the only stable fixed point of the expected next population operator corresponds to the population consisting entirely of the optimal string. This result is then extended by way of a perturbation argument to allow nonzero mutation.
A Description of Holland's Royal Road Function
Authors: Jones Terry
Published: February 2008

This paper contains a description of John Holland's Royal Road function, which was presented at the Fifth International Conference on Genetic Algorithms in July 1993 and posted to the Internet Genetic Algorithms mailing list in August 1993.
Genetic Algorithms as Global Random Search Methods: An Alternative Perspective
Authors: Peck Charles C. , Dhawan Atam P.
Published: February 2008

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
Toward a Theory of Evolution Strategies: On the Benefits of Sex— the (μ/μ, λ) Theory
Authors: Beyer Hans-Georg
Published: February 2008

The multirecombinant (μ/μ, λ) evolution strategy (ES) is investigated for real-valued, N-dimensional parameter spaces. The analysis includes both intermediate recombination and dominant recombination, as well. These investigations are done for the spherical model first. The problem of the optimal population size depending on the parameter space dimension N is solved. A method extending the results obtained for the spherical model to nonspherical success domains is presented.

The power of sexuality is discussed and it is shown that this power does not stem mainly from the “combination” of “good properties” of the mates (building block hypothesis) but rather from genetic repair diminishing the influence of harmful mutations. The dominant recombination is analyzed by introduction of surrogate mutations leading to the concept of species. Conclusions for evolutionary algorithms (EAs), including genetic algorithms (GAs), are drawn.
Solving Pattern Nesting Problems with Genetic Algorithms Employing Task Decomposition and Contact Detection
Authors: Dighe Rahul , Jakiela Mark J.
Published: February 2008

A hierarchical approach for nesting two-dimensional shapes based on genetic algorithms is described. For the higher-level search, a representation that facilitates genetic search based on recombination is developed. An alternatiye to overlap computation based on assembly of polygons is used at the lower level of search. Two implementations to find minimum-area enclosures for polygons, with and without a cohstraint on the width of stock, are discussed. Sample output illustrating the effectiveness of the approach is provided.
Finiteness of the Fixed Point Set for the Simple Genetic Algorithm
Authors: Wright Alden H. , Vose Michael D.
Published: February 2008

The infinite population simple genetic algorithm is a discrete dynamical system model of a genetic algorithm. It is conjectured that trajectories in the model always converge to fixed points. This paper shows that an arbitrarily small perturbation of the fitness will result in a model with a finite number of fixed points. Moreover, every sufficiently small perturbation of fimess preserves the finiteness of the fixed point set. These results allow proofs and constructions that require finiteness of the fixed point set. For example, applying the stable manifold theorem to a fixed point requires the hyperbolicity of the differential of the transition map of the genetic algorithm, which requires (among other things) that the fixed point be isolated.
Analyzing the (1, λ) Evolution Strategy via Stochastic Approximation Methods
Authors: Yin G. , Rudolph G. , Schwefel H.-P,
Published: February 2008

The main objective of this paper is to analyze the (1, λ) evolution strategy by use of stochastic approximation methods. Both constant and decreasing step size algorithms are studied. Convergence and estimation error bounds for the (1, λ) evolution strategy are developed. First the algorithm is converted to a recursively defined scheme of stochastic approximation type. Then the analysis is carried out by using the analytic tools from stochastic approximation. In lieu of examining the discrete iterates, suitably scaled sequences are defined. These interpolated sequences are then studied in detail. It is shown that the limits of the sequences have natural connections to certain continuous time dynamical systems.
Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise
Authors: Miller Brad L. , Goldberg David E.
Published: February 2008

This paper analyzes the effect of noise on different selection mechanisms for genetic algorithms (GAs). Models for several selection schemes are developed that successfully predict the convergence characteristics of GAs within noisy environments. The selection schemes modeled in this paper include proportionate selection, tournament selection, (μ, λ) selection, and linear ranking selection. An allele-wise model for convergence in the presence of noise is developed for the OneMax domain, and then extended to more complex domains where the building blocks are uniformly scaled. These models are shown to accurately predict the convergence rate of GAs for a wide range of noise levels.
How to Shift Bias: Lessons from the Baldwin Effect
Authors: Turney Peter
Published: February 2008

An inductive learning algorithm takes a set of data as input and generates a hypothesis as output. A set of data is typically consistent with an infinite number of hypotheses; therefore, there must be factors other than the data that determine the output of the learning algorithm. In machine learning, these other factors are called the bias of the learner. Classical learning algorithms have a fixed bias, implicit in their design. Recently developed learning algorithms dynamically adjust their bias as they search for a hypothesis. Algorithms that shift bias in this manner are not as well understood as classical algorithms. In this paper, we show that the Baldwin effect has implications for the design and analysis of bias shifting algorithms. The Baldwin effect was proposed in 1896 to explain how phenomena that might appear to require Lamarckian evolution (inheritance of acquired characteristics) can arise from purely Darwinian evolution. Hinton and Nowlan presented a computational model of the Baldwin effect in 1987. We explore a variation on their model, which we constructed explicitly to illustrate the lessons that the Baldwin effect has for research in bias shifting algorithms. The main lesson is that it appears that a good strategy for shift of bias in a learning algorithm is to begin with a weak bias and gradually shift to a strong bias.
Is There Another New Factor in Evolution?
Authors: Harvey Inman
Published: February 2008

For 100 years it has been recognized that interactions between learning and evolution, such as the Baldwin effect, can be subtle and often counterintuitive. Recently a new effect has been discussed: It is suggested that evolutionary progress toward one specific goal may be assisted by lifetime learning on a different task that may or may not be “uncorrelated.” The phenomenon is reproduced here in a simple scenario, where the tasks are indeed uncorrelated—‘Another New Factor’ does indeed exist. The effect is then explained as being due to recovery from weight perturbations caused by mutation in a neural network. It is a special case of a recently discovered relearning effect: the spontaneous recovery of perturbed associations by learning uncorrelated tasks.
A Measure of Landscapes
Authors: Hordijk Wim
Published: February 2008

This paper introduces a statistical fitness landscape analysis, based on Weinberger's random walk method and on a time series analysis known as the Box-Jenkins approach, to measure and express the correlation structure of fitness landscapes. The analysis has some additions to and advantages over previous methods for measuring this structure. The analysis is demonstrated on fitness landscapes constructed with Kauffman's NK-model, using two operators (point mutation and a form of crossover) and a combination of the two. Furthermore, the predictive value of the method is shown.
A Comparison of Selection Schemes Used in Evolutionary Algorithms
Authors: Blickle Tobias , Thiele Lothar
Published: February 2008

Evolutionary algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme, for which in this paper a new description model, based on fitness distributions, is introduced. With this, a mathematical analysis of tournament selection, truncation selection, ranking selection, and exponential ranking selection is carried out that allows an exact prediction of the fitness values after selection. The correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proved. Furthermore, several properties of selection schemes are derived (selection intensity, selection variance, loss of diversity), and the three selection schemes are compared using these properties.
Some Comments on Evolutionary Algorithm Theory
Authors: Salomon Ralf
Published: February 2008

The development of a sound theory that predicts and verifies existing evolutionary algorithms (EA) is one of the most important research issues in the field today. In mathematical proofs, the assumption of spherical symmetry is probably one of the most widely used simplifications. This paper discusses the extent to which spherical symmetry is appropriate for certain EAs. It turns out that spherical symmetry leads to simplifications in (self-adaptive) EAs but seems inappropriate for certain genetic algorithm variants, since small mutation rates bias a search algorithm toward the coordinate axes. This paper also argues that current test suites are weak in that they do not provide problems with significant epistasis that describes the interaction between different parameters. Consequently, when using an empirical test for pushing existing theory beyond its limits, benchmark functions should include more epistatic interaction or at least should use coordinate rotations.
Empirical Investigation of the Benefits of Partial Lamarckianism
Authors: Houck Christopher R. , Joines Jeffery A. , Kay Michael G. , Wilson James R.
Published: February 2008

Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid GAs are the combination of improvement procedures, which are good at finding local optima, and GAs. There are two basic strategies for using hybrid GAs. In the first, Lamarckian learning, the genetic representation is updated to match the solution found by the improvement procedure. In the second, Baldwinian learning, improvement procedures are used to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper examines the issue of using partial Lamarckianism (i.e., the updating of the genetic representation for only a percentage of the individuals), as compared to pure Lamarckian and pure Baldwinian learning in hybrid GAs. Multiple instances of five bounded nonlinear problems, the location-allocation problem, and the cell formation problem were used as test problems in an empirical investigation. Neither a pure Lamarckian nor a pure Baldwinian search strategy was found to consistently lead to quicker convergence of the GA to the best known solution for the series of test problems. Based on a minimax criterion (i.e., minimizing the worst case performance across all test problem instances), the 20% and 40% partial Lamarckianism search strategies yielded the best mixture of solution quality and computational efficiency.
Forking Genetic Algorithms: GAs with Search Space Division Schemes
Authors: Tsutsui Shigeyoshi , Fujimoto Yoshiji , Ghosh Ashish
Published: February 2008

In this article, we propose a new type of genetic algorithm (GA), the forking GA (fGA), which divides the whole search space into subspaces, depending on the convergence status of the population and the solutions obtained so far. The fGA is intended to deal with multimodal problems that are difficult to solve using conventional GAs. We use a multi-population scheme that includes one parent population that explores one subspace and one or more child populations exploiting the other subspace. We consider two types of fGAs, depending on the method used to divide the search space. One is the genoqtypic fGA (g-fGA), which defines the search subspace for each subpopulation, depending on the salient schema within the genotypic search space. The other is the phenotypic fGA (p-fGA), which defines a search subspace by a neighborhood hypercube around the current best individual in the phenotypic feature space. Empirical results on complex function optimization problems show that both the g-fGA and the p-GA perform well compared to conventional GAs. Two additional utilities of the p-fGA are also studied briefly.
Multimodal Function Optimization Using Minimal Representation Size Clustering and Its Application to Planning Multipaths
Authors: Hocaoǧlu Cem , Sanderson Arthur C.
Published: February 2008

A novel genetic algorithm (GA) using minimal representation size cluster (MRSC) analysis is designed and implemented for solving multimodal function optimization problems. The problem of multimodal function optimization is framed within a hypothesize-and-test paradigm using minimal representation size (minimal complexity) for species formation and a GA. A multiple-population GA is developed to identify different species. The number of populations, thus the number of different species, is determined by the minimal representation size criterion. Therefore, the proposed algorithm reveals the unknown structure of the multimodal function when a priori knowledge about the function is unknown. The effectiveness of the algorithm is demonstrated on a number of multimodal test functions. The proposed scheme results in a highly parallel algorithm for finding multiple local minima. In this paper, a path-planning algorithm is also developed based on the MRSC-GA algorithm. The algorithm utilizes MRSC_GA for planning paths for mobile robots, piano-mover problems, and N-link manipulators. The MRSC_GA is used for generating multipaths to provide alternative solutions to the path-planning problem. The generation of alternative solutions is especially important for planning paths in dynamic environments. A novel iterative multiresolution path representation is used as a basis for the GA coding. The effectiveness of the algorithm is demonstrated on a number of two-dimensional path-planning problems.
Probabilistic Incremental Program Evolution
Authors: Salustowicz Rafal , Schmidhuber Jürgen
Published: February 2008

Probabilistic incremental program evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions, population-based incremental learning, and tree-coded programs like those used in some variants of genetic programming (GP). PIPE iteratively generates successive populations of functional programs according to an adaptive probability distribution over all possible programs. Each iteration, it uses the best program to refine the distribution. Thus, it stochastically generates better and better programs. Since distribution refinements depend only on the best program of the current population, PIPE can evaluate program populations efficiently when the goal is to discover a program with minimal runtime. We compare PIPE to GP on a function regression problem and the 6-bit parity problem. We also use PIPE to solve tasks in partially observable mazes, where the best programs have minimal runtime.
Evolutionary Induction of Sparse Neural Trees
Authors: Zhang Byoung-Tak , Ohm Peter , Mühlenbein Heinz
Published: February 2008

This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest.
Implicit Representation in Genetic Algorithms Using Redundancy
Authors: Raich Anne M. , Ghaboussi Jamshid
Published: February 2008

A new representation combining redundancy and implicit fitness constraints is introduced that performs better than a simple genetic algorithm (GA) and a structured GA in experiments. The implicit redundant representation (IRR) consists of a string that is over-specified, allowing for sections of the string to remain inactive during function evaluation. The representation does not require the user to prespecify the number of parameters to evaluate or the location of these parameters within the string. This information is obtained implicitly by the fitness function during the GA operations. The good performance of the IRR can be attributed to several factors: less disruption of existing fit members due to the increased probability of crossovers and mutation affecting only redundant material; discovery of fit members through the conversion of redundant material into essential information; and the ability to enlarge or reduce the search space dynamically by varying the number of variables evaluated by the fitness function. The IRR GA provides a more biologically parallel representation that maintains a diverse population throughout the evolution process. In addition, the IRR provides the necessary flexibility to represent unstructured problem domains that do not have the explicit constraints required by fixed representations.
The Equation for Response to Selection and Its Use for Prediction
Authors: Mühlenbein Heinz
Published: February 2008

The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population's fitness to the standard deviation of its fitness, as well as to the parameters selection intensity and realized heritability. In this paper the exact RS equation is derived for proportionate selection given an infinite population in linkage equilibrium. In linkage equilibrium the genotype frequencies are the product of the univariate marginal frequencies. The equation contains Fisher's fundamental theorem of natural selection as an approximation. The theorem shows that the response is approximately equal to the quotient of a quantity called additive genetic variance, VA, and the average fitness. We compare Mendelian two-parent recombination with gene-pool recombination, which belongs to a special class of genetic algorithms that we call univariate marginal distribution (UMD) algorithms. UMD algorithms keep the genotypes in linkage equilibrium. For UMD algorithms, an exact RS equation is proven that can be used for long-term prediction. Empirical and theoretical evidence is provided that indicates that Mendelian two-parent recombination is also mainly exploiting the additive genetic variance. We compute an exact RS equation for binary tournament selection. It shows that the two classical methods for estimating realized heritability—the regression heritability and the heritability in the narrow sense—may give poor estimates. Furthermore, realized heritability for binary tournament selection can be very different from that of proportionate selection. The paper ends with a short survey about methods that extend standard genetic algorithms and UMD algorithms by detecting interacting variables in nonlinear fitness functions and using this information to sample new points.
General Cardinality Genetic Algorithms
Authors: Koehler Gary J. , Bhattacharyya Siddhartha , Vose Michael D.
Published: February 2008

A complete generalization of the Vose genetic algorithm model from the binary to higher cardinality case is provided. Boolean AND and EXCLUSIVE-OR operators are replaced by multiplication and addition over rings of integers. Walsh matrices are generalized with finite Fourier transforms for higher cardinality usage. Comparison of results to the binary case are provided.
On the Futility of Blind Search: An Algorithmic View of “No Free Lunch”
Authors: Culberson Joseph C.
Published: February 2008

The paper is in three parts. First, we use simple adversary arguments to redevelop and explore some of the no-free-lunch (NFL) theorems and perhaps extend them a little. Second, we clarify the relationship of NFL theorems to algorithm theory and complexity classes such as NP. We claim that NFL is weaker in the sense that the constraints implied by the conjectures of traditional algorithm theory on what an evolutionary algorithm may be expected to accomplish are far more severe than those implied by NFL. Third, we take a brief look at how natural evolution relates to computation and optimization. We suggest that the evolution of complex systems exhibiting high degrees of orderliness is not equivalent in difficulty to optimizing hard (in the complexity sense) problems, and that the optimism in genetic algorithms (GAs) as universal optimizers is not justified by natural evolution.

This is an informal tutorial paper—most of the information presented is not formally proven, and is either “common knowledge” or formally proven elsewhere. Some of the claims are intuitions based on experience with algorithms, and in a more formal setting should be classified as conjectures.