bluuebunny/crossref_metadata_2025 huggingface dataset:
Endothelial Nitric Oxide Synthase Polymorphism (−786T→C) and Increased Risk of Angiographic Vasospasm After Aneurysmal Subarachnoid Hemorrhage
Authors: Ko Nerissa U. , Rajendran Pam , Kim Helen , Rutkowski Martin , Pawlikowska Ludmila , Kwok Pui-Yan , Higashida Randall T. , Lawton Michael T. , Smith Wade S. , Zaroff Jonathan G. , Young William L.
Published: February 2008

Background and Purpose—

Vasospasm after aneurysmal subarachnoid hemorrhage (SAH) remains a leading cause of death and disability after aneurysm rupture. Decreased availability of nitric oxide (NO) may be crucial in its pathogenesis. We hypothesized that endothelial NO synthase (
eNOS
) polymorphisms may determine susceptibility to vasospasm in SAH patients.

Methods—

We conducted a prospective cohort study of SAH patients and determined vasospasm by cerebral angiography. We genotyped 3
eNOS
polymorphisms: an intron 4 variable-number tandem-repeat, a promoter single-nucleotide polymorphism (−786T→C SNP), and a coding SNP in exon 7 (894G→T encoding E298D). Using multivariable logistic regression, we quantified the association of
eNOS
polymorphisms in patients with vasospasm confirmed by cerebral angiography.

Results—

For the
eNOS
promoter −786T→C SNP, the presence of the
CC
genotype compared with any
T
genotype (
CT
or
TT
) was associated with increased odds of vasospasm (odds ratio=2.97, 95% CI=1.32 to 6.67,
P
=0.008). No association with vasospasm was observed for the
eNOS
894G→T or variable-number tandem-repeat polymorphisms.

Conclusions—

These findings suggest that genetic variation influencing NO regulation contributes to the risk of angiographic vasospasm in patients with SAH. The specific role of the promoter SNP (−786T→C) may determine the effect of NO regulated by this pathway, distinct from other known
eNOS
polymorphisms.
Xenon and Hypothermia Combine Additively, Offering Long-Term Functional and Histopathologic Neuroprotection After Neonatal Hypoxia/Ischemia
Authors: Hobbs Catherine , Thoresen Marianne , Tucker Alexander , Aquilina Kristian , Chakkarapani Ela , Dingley John
Published: February 2008

Background and Purpose—

Hypoxic/ischemic (HI) brain injury affects 1 to 6 per 1000 live human births, with a mortality of 15% to 20%. A quarter of survivors have permanent disabilities. Hypothermia is the only intervention that improves outcome; however, further improvements might be obtained by combining hypothermia with additional treatments. Xenon is a noble anesthetic gas with an excellent safety profile, showing great promise in vitro and in vivo as a neuroprotectant. We investigated combinations of 50% xenon (Xe
50%
) and hypothermia of 32°C (HT
32°C
) as a post-HI therapy.

Methods—

An established neonatal rat HI model was used. Serial functional neurologic testing into adulthood 10 weeks after injury was performed, followed by global and regional brain histopathology evaluation.

Results—

In the combination Xe
50%
HT
32°C
group, complete restoration of long-term functional outcomes was seen. Hypothermia produced improvement on short- (
P
<0.001) and long- (
P
<0.001) term functional testing, whereas Xe
50%
alone predominantly improved long-term function (
P
<0.05), suggesting that short-term testing does not always predict eventual outcome. Similarly, the Xe
50%
HT
32°C
combination produced the greatest (71%) improvement in global histopathology scores, a pattern mirrored in the regional scores, whereas Xe
50%
and HT
32°C
individually produced smaller improvements (
P
<0.05 and
P
<0.001, respectively). The interaction between the 2 treatments was additive.

Conclusions—

The xenon/hypothermia combination additively confers greater protection after HI than either treatment alone. The functional improvement is almost complete, is sustained long term, and is accompanied by greatly improved histopathology. The unique safety profile differentiates xenon as an attractive combination therapy with hypothermia to improve the otherwise bleak outcome from neonatal HI.
Intracarotid Injection of Fluorescence Activated Cell-Sorted CD49d-Positive Neural Stem Cells Improves Targeted Cell Delivery and Behavior After Stroke in a Mouse Stroke Model
Authors: Guzman Raphael , De Los Angeles Alejandro , Cheshier Samuel , Choi Raymond , Hoang Stanley , Liauw Jason , Schaar Bruce , Steinberg Gary
Published: February 2008

Background and Purpose—

Intravascular delivery of neural stem cells (NSCs) after stroke has been limited by the low efficiency of transendothelial migration. Vascular cell adhesion molecule-1 is an endothelial adhesion molecule known to be upregulated early after stroke and is responsible for the firm adhesion of inflammatory cells expressing the surface integrin, CD49d. We hypothesize that enriching for NSCs that express CD49d and injecting them into the carotid artery would improve targeted cell delivery to the injured brain.

Methods—

Mouse NSCs were analyzed for the expression of CD49d by fluorescence activated cell sorting. A CD49d-enriched (CD49d
+
) (>95%) and -depleted (CD49d
−
; <5%) NSC population was obtained by cell sorting. C57/Bl6 mice underwent left-sided hypoxia–ischemia surgery and were assigned to receive 3×10
5
CD49d
+
, CD49d
−
NSCs, or vehicle injection into the left common carotid artery 48 hours after stroke. Behavioral recovery was measured using a rotarod for 2 weeks after cell injection.

Results—

Fluorescence activated cell sorting analysis revealed 25% CD49d
+
NSCs. In a static adhesion assay, NSCs adhered to vascular cell adhesion molecule-1 in a dose-dependent manner. Significantly more NSCs were found in the cortex, the hippocampus, and the subventricular zone in the ischemic hemisphere in animals receiving CD49d
+
NSCs as compared with CD49d
−
NSCs (
P
<0.05). Animals treated with CD49d
+
cells showed a significantly better behavioral recovery as compared with CD49d
−
and vehicle-treated animals.

Conclusions—

We show that enrichment of NSCs by fluorescence activated cell sorting for the surface integrin, CD49d, and intracarotid delivery promotes cell homing to the area of stroke in mice and improves behavioral recovery.
The Role of Aquaporin-4 Polymorphisms in the Development of Brain Edema After Middle Cerebral Artery Occlusion
Authors: Kleffner Ilka , Bungeroth May , Schiffbauer Hagen , Schäbitz Wolf-Ruediger , Ringelstein E. Bernd , Kuhlenbäumer Gregor
Published: February 2008

Background and Purpose—

Some patients develop severe brain edema after complete middle cerebral artery occlusion, whereas others do not. Aquaporin-4 (
AQP4
) is the main water channel in the brain and has been shown to be critical for the development of brain edema after ischemia. We asked whether genetic variation in the
AQP4
gene is related to the severity of brain edema after middle cerebral artery occlusion.

Methods—

We genotyped 10 single nucleotide polymorphisms distributed across the
AQP4
gene in 41 patients with middle cerebral artery occlusion with and without severe brain edema and assessed single marker association as well as the linkage dysequilibrium structure across
AQP4
.

Results—

One single nucleotide polymorphism (rs9951307) at the 3′ end of
AQP4
was associated with severe brain edema (dominant model,
P
=0.01; OR, 0.10; 95% CI, 0.02 to 0.49 for the protective G-allele). Linkage dysequilibrium across
AQP4
was low; no clear haplotype blocks could be identified for the assessment of haplotype association.

Conclusions—

This explorative study shows that genetic variation in
AQP4
might contribute to brain edema formation after middle cerebral artery occlusion and warrants further investigation.
Insulin Growth Factor-1 Gene Transfer Enhances Neurovascular Remodeling and Improves Long-Term Stroke Outcome in Mice
Authors: Zhu Wei , Fan Yongfeng , Frenzel Tim , Gasmi Mehdi , Bartus Raymond T. , Young William L. , Yang Guo-Yuan , Chen Yongmei
Published: February 2008

Background and Purpose—

Insulin-like growth factor I (IGF-1) is a pleiotropic growth factor that has been demonstrated to protect against acute ischemic brain injury. Whether IGF-1 improves long-term functional outcome after ischemic stroke is not known. The aim of this study is to examine whether IGF-1 overexpression through adeno-associated virus (AAV) -mediated gene transfer enhances neurovascular remodeling and improves functional outcome in a mouse model of focal cerebral ischemia.

Methods—

Long-term cerebral IGF-1 overexpression was achieved with the AAV transduction system through stereotaxic injection. Control mice were injected with AAV–green fluorescent protein or saline. Three weeks after gene transfer, the mice underwent permanent distal middle cerebral artery occlusion. Histological and behavioral analyses were performed at day 21 after middle cerebral artery occlusion.

Results—

IGF-1 gene transfer compared with control treatment significantly improved motor performance assessed by sensorimotor tests. The functional recovery was accompanied by reduced volume of cerebral infarction. Immunohistochemical analysis with endothelial cell marker CD31 revealed that IGF-1 gene transfer potently increased neovessel formation in the periinfarct and injection needle tract area compared with AAV–green fluorescent protein transduction. Increased vascular density was associated with increased local vascular perfusion. Additionally, AAV-IGF-1 treatment enhanced neurogenesis in the subventricular zone compared with AAV–green fluorescent protein treatment.

Conclusions—

These data demonstrate that IGF-1 overexpression promoted long-lasting functional recovery after cerebral infarction. The improved functional performance was paralleled by enhanced neovascularization and neurogenesis.
Epidemiological Transition of Stroke in China
Authors: Zhao Dong , Liu Jing , Wang Wei , Zeng Zhechun , Cheng Jun , Liu Jun , Sun Jiayi , Wu Zhaosu
Published: February 2008

Background and Purpose—

Epidemiological patterns of stroke may change with economic development. It is important to understand these changes for making timely strategies for stroke prevention. The aim of this study was to examine the changes in trends of stroke epidemiology during a period of fast economic development in China, based on data of the Sino-MONICA-Beijing project.

Methods—

Acute stroke events were registered in a large defined population aged 25 through 74 years from 1984 to 2004. The age standardized incidence rates and case fatality rates of total stroke, ischemic stroke and hemorrhagic stroke were calculated. The trends were analyzed by a regression model.

Results—

There was a total of 14 584 stroke events registered in the study population from 1984 through 2004. The incidence rate of hemorrhagic stroke declined by 1.7% and the incidence rate of ischemic stroke increased by 8.7% annually on average. The case fatality rates significantly reduced in both types of stroke. The mean onset age of stroke was delayed by 2.7 years in men and 3.6 years in women. The proportion of deaths of cerebrovascular disease out of total deaths decreased and the proportion of ischemic heart disease increased during the study period.

Conclusions—

Characteristics of stroke transition were found during a period of economic development in China. The changes in patterns of stroke have raised new challenges and the need for priority adjustment for stroke prevention in China.
Simvastatin Suppresses the Progression of Experimentally Induced Cerebral Aneurysms in Rats
Authors: Aoki Tomohiro , Kataoka Hiroharu , Ishibashi Ryota , Nozaki Kazuhiko , Hashimoto Nobuo
Published: February 2008

Background and Purpose—

The pathophysiology of cerebral aneurysms (CAs) is linked to chronic inflammation and degradation of extracellular matrix in vascular walls. Because statins have protective effects on various vascular diseases independent of their lipid-lowering effects, we investigated the effect of simvastatin on CA progression.

Methods—

CAs were induced in Sprague-Dawley rats with or without oral administration of simvastatin. The size and media thickness of CAs was evaluated 3 months after aneurysm induction. Expression of macrophage chemoattractant protein-1, vascular cell adhesion molecule-1, endothelial nitric oxide synthase, interleukin-1β, inducible nitric oxide synthase, matrix metalloproteinase-2, and matrix metalloproteinase-9 in aneurysmal walls was examined by reverse transcriptase–polymerase chain reaction and immunohistochemistry. To examine whether simvastatin has a suppressive effect on preexisting CAs, simvastatin administration started at 1 month after aneurysm induction.

Results—

Rats treated with simvastatin exhibited a significant increase in media thickness and a significant reduction in aneurysmal size compared with control rats. Treatment with simvastatin resulted in reduced expression of macrophage chemoattractant protein-1 and vascular cell adhesion molecule-1, increased expression of endothelial nitric oxide synthase, and reduced the number of macrophage infiltration. In quantitative polymerase chain reaction and immunohistochemistry, simvastatin significantly inhibited upregulated expression of interleukin-1β, inducible nitric oxide synthase, matrix metalloproteinase-2, and matrix metalloproteinase-9 associated with CA progression. Gelatin zymography revealed decreased activity of matrix metalloproteinase-2 and matrix metalloproteinase-9 in aneurysmal walls by simvastatin treatment. Simvastatin also effectively inhibited aneurysm enlargement and thinning of the media of preexisting CAs.

Conclusions—

Treatment with simvastatin suppresses the development of CAs by inhibiting inflammatory reactions in aneurysmal walls. Simvastatin also has a preventive effect on the progression of preexisting CAs. Simvastatin is a promising candidate of a novel medical treatment for the prevention of CA progression.
Complete Ophthalmoplegia
Authors: Thurtell Matthew J. , Halmagyi G. Michael
Published: February 2008

Background and Purpose—

Complete ophthalmoplegia, the combination of bilateral ptosis with loss of all extraocular movements, is rarely a consequence of ischemic stroke. We describe 3 patients who had complete ophthalmoplegia as a manifestation of bilateral paramedian midbrain-thalamic infarction, and we discuss possible pathophysiologic mechanisms.

Summary of Cases—

Three patients presented with coma. All had complete ophthalmoplegia that initially persisted despite improvement or fluctuation in their other deficits. MRI revealed bilateral paramedian midbrain-thalamic infarction. Two patients died, with the ophthalmoplegia remaining unchanged before death. The surviving patient had a progressive improvement in ocular abduction but persisting third nerve and vertical gaze palsies.

Conclusions—

Complete ophthalmoplegia is an unusual sign of bilateral paramedian midbrain-thalamic infarction. The ophthalmoplegia could result from combined third nerve, pseudoabducens, and vertical gaze palsies.
Stroke Program Review Group
Authors: Grotta James C. , Jacobs Thomas P. , Koroshetz Walter J. , Moskowitz Michael A.
Published: February 2008

The Stroke Program Review Group (SPRG) met for the first time in 2001 to identify and prioritize scientific needs and opportunities in stroke, and to consider strategies to address them. Out of this meeting came a set of scientific and resource recommendations (http://www.ninds.nih.gov/find_people/groups/stroke_prg/index.htm) including the recommendation to review progress after 5 years. In September 2006, the NINDS convened a second SPRG of prominent scientists, clinicians, industry leaders, and patient advocates to discuss progress since the first SPRG and to examine new opportunities in light of more recent advances. The report of the second SPRG meeting is found at http://www.ninds.nih.gov/find_people/groups/stroke_prg/09_2006_stroke_prg_report.htm. In this brief interim report, we list and summarize progress in each of the investigational domains outlined in the SPRG with an emphasis on those advances directly resulting from NINDS-funded research. Also listed are the top 2 or 3 research priorities for the next 5-year cycle for each investigational theme. What emerges from the first 2 reports is a clear set of priorities that have since been complemented by exciting advances that are likely to be the focus of a follow-up report from the next SPRG in 2010.
An Overview of Evolutionary Algorithms for Parameter Optimization
Authors: Bäck Thomas , Schwefel Hans-Paul
Published: February 2008

Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs), evolutionary programming (EP), and genetic algorithms (GAs). The comparison is performed with respect to certain characteristic components of EAs: the representation scheme of object variables, mutation, recombination, and the selection operator. Furthermore, each algorithm is formulated in a high-level notation as an instance of the general, unifying basic algorithm, and the fundamental theoretical results on the algorithms are presented. Finally, after presenting experimental results for three test functions representing a unimodal and a multimodal case as well as a step function with discontinuities, similarities and differences of the algorithms are elaborated, and some hints to open research questions are sketched.
Toward a Theory of Evolution Strategies: Some Asymptotical Results from the (1,+ λ)-Theory
Authors: Beyer Hans-Georg
Published: February 2008

A method for the determination of the progress rate and the probability of success for the Evolution Strategy (ES) is presented. The new method is based on the asymptotical behavior of the χ-distribution and yields exact results in the case of infinite-dimensional parameter spaces. The technique is demonstrated for the (l,+ λ) ES using a spherical model including noisy quality functions. The results are used to discuss the convergence behavior of the ES.
Physically Realistic Motion Synthesis in Animation
Authors: Ngo J. Thomas , Marks Joe
Published: February 2008

Motion-synthesis problems arise in the creation of physically realistic animations involving autonomous characters. Trpically characters are required to perform goal tasks, subject to physical law and other constraints on their motion. Witkin and Kass (1988) dubbed this class of problems “Spacetime Constraints“ (SC) and presented results for specific problems involving an articulated figure. Their approach was based on a procedure for the local optimization of an initial approximate trajectory supplied by the user. Unfortunately, SC problems are typically multimodal and discontinuous, and the number of decision alternatives available at each time step can be exponential in the number of degrees of freedom in the system. Thus, constructing even coarse trajectories for subsequent optimization can be difficult.

We present an algorithm that constructs such trajectories de novo, without directive input from the user. Rather than use a time-series representation, which might be appropriate for local optimization, our algorithm uses a stimulus-response model. Locomotive skills appropriate for the given articulated figure are acquired through repeated testing of (simulated) reality. Our initial implementation, which chooses stimulus-response parameters using a parallel genetic algorithm, succeeds in finding good, novel solutions for a test suite of SC problems involving unbranched 2-D linkages.
Massively Parallel Simulated Annealing and Its Relation to Evolutionary Algorithms
Authors: Rudolph Günter
Published: February 2008

Simulated annealing and single-trial versions of evolution strategies possess a close relationship when they are designed for optimization over continuous variables. Analytical investigations of their differences and similarities lead to a cross-fertilization of both approaches, resulting in new theoretical results, new parallel population-based algorithms, and a better understanding of the interrelationships.
ZCS: A Zeroth Level Classifier System
Authors: Wilson Stewart W.
Published: February 2008

A basic classifier system, ZCS, is presented that keeps much of Holland's original framework but simplifies it to increase understandability and performance. ZCS's relation to Q-learning is brought out, and their performances compared in environments of two difficulty levels. Extensions to ZCS are proposed for temporary memory, better action selection, more efficient use of the genetic algorithm, and more general classifier representation.
Using Coverage as a Model Building Constraint in Learning Classifier Systems
Authors: Greene David Perry , Smith Stephen F.
Published: February 2008

Promoting and maintaining diversity is a critical requirement of search in learning classifier systems (LCSs). What is required of the genetic algorithm (GA) in an LCS context is not convergence to a single global maximum, as in the standard optimization framework, but instead the generation of individuals (i.e., rules) that collectively cover the overall problem space. COGIN (COverage-based Genetic INduction) is a system designed to exploit genetic recombination for the purpose of constructing rule-based classification models from examples. The distinguishing characteristic of COGIN is its use of coverage of training set examples as an explicit constraint on the search, which acts to promote appropriate diversity in the population of rules over time. By treating training examples as limited resources, COGIN creates an ecological model that simultaneously accommodates a dynamic range of niches while encouraging superior individuals within a niche, leading to concise and accurate decision models. Previous experimental studies with COGIN have demonstrated its performance advantages over several well-known symbolic induction approaches. In this paper, we examine the effects of two modifications to the original system configuration, each designed to inject additional diversity into the search: increasing the carrying capacity of training set examples (i.e., increasing coverage redundancy) and increasing the level of disruption in the recombination operator used to generate new rules. Experimental results are given that show both types of modifications to yield substantial improvements to previously published results.
Changing Representations During Search: A Comparative Study of Delta Coding
Authors: Mathias Keith E. , Whitley L. Darrell
Published: February 2008

Delta coding is an iterative genetic search strategy that dynamically changes the representation of the search space in an attempt to exploit different problem representations. Delta coding sustains search by reinitializing the population at each iteration of search. This helps to avoid the asymptotic performance typically observed in genetic search as the population becomes more homogeneous. Here, the optimization ability of delta coding is empirically compared against CHC, ESGA, GENITOR, and random mutation hill-climbing (RMHC) on a suite of well-known test functions with and without Gray coding. Issues concerning the effects of Gray coding on these test functions are addressed.
Mutation-Crossover Isomorphisms and the Construction of Discriminating Functions
Authors: Culberson Joseph C.
Published: February 2008

We compare the search power of crossover and mutation in genetic algorithms. Our discussion is framed within a model of computation using search space structures induced by these operators. Isomorphisms between the search spaces generated by these operators on small populations are identified and explored. These are closely related to the binary reflected Gray code. Using these we generate discriminating functions that are hard for one operator but easy for the other and show how to transform from one case to the other.

We use these functions to provide theoretical evidence that traditional GAs use mutation more effectively than crossover, but dispute claims that mutation is a better search mechanism than crossover. To the contrary, we show that methods that exploit crossover more effectively can be designed and give evidence that these are powerful search mechanisms. Experimental results using GIGA, the Gene Invariant Genetic Algorithm, and the well-known GENESIS program support these theoretical claims.

Finally, this paper provides the initial approach to a different method of analysis of GAs that does not depend on schema analysis or the notions of increased allocations of trials to hyperplanes of above-average fitness. Instead it focuses on the search space structure induced by the operators and the effect of a population search using them.
A Derandomized Approach to Self-Adaptation of Evolution Strategies
Authors: Ostermeier Andreas , Gawelczyk Andreas , Hansen Nikolaus
Published: February 2008

Comparable to other optimization techniques, the performance of evolution strategies (ESs) depends on a suitable choice of internal strategy control parameters. Apart from a fixed setting, ESs facilitate an adjustment of such parameters within a self-adaptation process. For step-size control in particular, various adaptation concepts have been evolved early in the development of ESs. These algorithms mostly work very efficiently as long as the scaling of the parameters to be optimized is known. If the scaling is not known, the strategy has to adapt individual step-sizes for all the parameters. In general, the number of necessary step-sizes (variances) equals the dimension of the problem. In this case, step-size adaptation proves to be difficult, and the algorithms known are not satisfactory.

The algorithm presented in this paper is based on the well-known concept of mutative step-size control. Our investigations indicate that the adaptation by this concept declines due to an interaction of the random elements involved. We show that this weak point of mutative step-size control can be avoided by relatively small changes in the algorithm. The modifications may be summarized by the word “derandomization.” The derandomized scheme of mutative step-size control facilitates a reliable self-adaptation of individual step-sizes.
An Overview of Evolutionary Algorithms in Multiobjective Optimization
Authors: Fonseca Carlos M. , Fleming Peter J.
Published: February 2008

The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, that is, number of offspring.

In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality. The sensitivity of different methods to objective scaling and/or possible concavities in the trade-off surface is considered, and related to the (static) fitness landscapes such methods induce on the search space. From the discussion, directions for future research in multiobjective fitness assignment and search strategies are identified, including the incorporation of decision making in the selection procedure, fitness sharing, and adaptive representations.
Balancing Accuracy and Parsimony in Genetic Programming
Authors: Zhang Byoung-Tak , Mühlenbein Heinz
Published: February 2008

Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this article we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks.
Strongly Typed Genetic Programming
Authors: Montana David J.
Published: February 2008

Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection (Koza, 1992). However, in its standard form, there is no way to restrict the programs it generates to those where the functions operate on appropriate data types. In the case when the programs manipulate multiple data types and contain functions designed to operate on particular data types, this can lead to unnecessarily large search times and/or unnecessarily poor generalization performance. Strongly typed genetic programming (STGP) is an enhanced version of genetic programming that enforces data-type constraints and whose use of generic functions and generic data types makes it more powerful than other approaches to type-constraint enforcement. After describing its operation, we illustrate its use on problems in two domains, matrix/vector manipulation and list manipulation, which require its generality. The examples are (1) the multidimensional least-squares regression problem, (2) the multidimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR.
A Comparison of the Fixed and Floating Building Block Representation in the Genetic Algorithm
Authors: Wu Annie S. , Lindsay Robert K.
Published: February 2008

This article compares the traditional, fixed problem representation style of a genetic algorithm (GA) with a new floating representation in which the building blocks of a problem are not fixed at specific locations on the individuals of the population. In addition, the effects of noncoding segments on both of these representations is studied. Noncoding segments are a computational model of noncoding deoxyribonucleic acid, and floating building blocks mimic the location independence of genes. The fact that these structures are prevalent in natural genetic systems suggests that they may provide some advantages to the evolutionary process. Our results show that there is a significant difference in how GAs solve a problem in the fixed and floating representations. Genetic algorithms are able to maintain a more diverse population with the floating representation. The combination of noncoding segments and floating building blocks appears to encourage a GA to take advantage of its parallel search and recombination abilities.
Landscapes, Learning Costs, and Genetic Assimilation
Authors: Mayley Giles
Published: February 2008

The evolution of a population can be guided by phenotypic traits acquired by members of that population during their lifetime. This phenomenon, known as the Baldwin effect, can speed the evolutionary process as traits that are initially acquired become genetically specified in later generations. This paper presents conditions under which this genetic assimilation can take place. As well as the benefits that lifetime adaptation can give a population, there may be a cost to be paid for that adaptive ability. It is the evolutionary trade-off between these costs and benefits that provides the selection pressure for acquired traits to become genetically specified. It is also noted that genotypic space, in which evolution operates, and phenotypic space, in which adaptive processes (such as learning) operate, are, in general, of a different nature. To guarantee that an acquired characteristic can become genetically specified, these spaces must have the property of neighborhood correlation, which means that a small distance between two individuals in phenotypic space implies that there is a small distance between the same two individuals in genotypic space.
Modeling the Evolution of Motivation
Authors: Batali John , Grundy William Noble
Published: February 2008

For learning to improve the adaptiveness of an animal's behavior, and thus direct evolution in the way Baldwin suggested, the learning mechanism must incorporate an innate evaluation of how the animal's actions influence its reproductive fitness. For example, many circumstances that damage an animal or otherwise reduce its fitness are painful and tend to be avoided. We refer to the mechanism by which an animal evaluates the fitness consequences of its actions as a “motivation system,” and argue that such a system must evolve along with the behaviors it evaluates. We describe simulations of the evolution of populations of agents instantiating a number of different architectures for generating action and learning in worlds of differing complexity. We find that in some cases, members of the populations evolve motivation systems that are accurate enough to direct learning so as to increase the fitness of the actions that the agents perform. Furthermore, the motivation systems tend to incorporate systematic distortions in their representations of the worlds they inhabit; these distortions can increase the adaptiveness of the behavior generated.
New Methods for Competitive Coevolution
Authors: Rosin Christopher D. , Belew Richard K.
Published: February 2008

We consider “competitive coevolution,” in which fitness is based on direct competition among individuals selected from two independently evolving populations of “hosts” and “parasites.” Competitive coevolution can lead to an “arms race,” in which the two populations reciprocally drive one another to increasing levels of performance and complexity. We use the games of Nim and 3-D Tic-Tac-Toe as test problems to explore three new techniques in competitive coevolution. “Competitive fitness sharing” changes the way fitness is measured; “shared sampling” provides a method for selecting a strong, diverse set of parasites; and the “hall of fame” encourages arms races by saving good individuals from prior generations. We provide several different motivations for these methods and mathematical insights into their use. Experimental comparisons are done, and a detailed analysis of these experiments is presented in terms of testing issues, diversity, extinction, arms race progress measurements, and drift.
Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder
Authors: Hart Emma , Ross Peter , Nelson Jeremy
Published: February 2008

This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a “permutation + schedule builder” by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that population-based methods are superior to both hill-climbing and simulated annealing in the quality of solutions produced. Population-based methods also have the distinct advantage of producing multiple, equally fit solutions, which is of particular importance when considering the practical aspects of the problem.
Initialization Strategies and Diversity in Evolutionary Timetabling
Authors: Burke Edmund K. , Newall James P. , Weare Rupert F.
Published: February 2008

This document seeks to provide a scientific basis by which different initialization algorithms for evolutionary timetabling may be compared. Seeding the initial population may be used to improve initial quality and provide a better starting point for the evolutionary algorithm. This must be tempered against the consideration that if the seeding algorithm produces very similar solutions, then the loss of genetic diversity may well lead to a worse final solution. Diversity, we hope, provides a good indication of how good the final solution will be, although only by running the evolutionary algorithm will the exact result be found. We will investigate the effects of heuristic seeding by taking quality and diversity measures of populations generated by heuristic initialization methods on both random and real-life data, as well as assessing the long-term performance of an evolutionary algorithm (found to work well on the timetabling problem) when using heuristic initialization. This will show how the use of heuristic initialization strategies can substantially improve the performance of evolutionary algorithms for the timetabling problem.
A Rigorous Complexity Analysis of the (1 + 1) Evolutionary Algorithm for Separable Functions with Boolean Inputs
Authors: Droste Stefan , Jansen Thomas , Wegener Ingo
Published: February 2008

Evolutionary algorithms (EAs) are heuristic randomized algorithms which, by many impressive experiments, have been proven to behave quite well for optimization problems of various kinds. In this paper a rigorous theoretical complexity analysis of the (1 + 1) evolutionary algorithm for separable functions with Boolean inputs is given. Different mutation rates are compared, and the use of the crossover operator is investigated. The main contribution is not the result that the expected run time of the (1 + 1) evolutionary algorithm is Θ(n ln n) for separable functions with n variables but the methods by which this result can be proven rigorously.
Putting More Genetics into Genetic Algorithms
Authors: Burke Donald S. , De Jong Kenneth A. , Grefenstette John J. , Ramsey Connie Loggia , Wu Annie S.
Published: February 2008

The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes.

Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve as scratch space in which VIV can explore alternative gene values. These results represent a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.
Production Scheduling and Rescheduling with Genetic Algorithms
Authors: Bierwirth Christian , Mattfeld Dirk C.
Published: February 2008

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed atreasonable runtime costs.
An Analysis of Generalization in the XCS Classifier System
Authors: Lanzi Pier Luca
Published: February 2008

The XCS classifier system represents a major advance in learning classifier systems research because (1) it has a sound and accurate generalization mechanism, and (2) its learning mechanism is based on Q-learning, a recognized learning technique. In taking XCS beyond its very first environments and parameter settings, we show that, in certain difficult sequential (“animat”) environments, performance is poor. We suggest that this occurs because in the chosen environments, some conditions for proper functioning of the generalization mechanism do not hold, resulting in overly general classifiers that cause reduced performance. We hypothesize that one such condition is a lack of sufficiently wide exploration of the environment during learning. We show that if XCS is forced to explore its environment more completely, performance improves dramatically. We propose a technique, based on Sutton's Dyna concept, through which wider exploration would occur naturally. Separately, we demonstrate that the compactness of the representation evolved by XCS is limited by the number of instances of each generalization actually present in the environment. The paper shows that XCS's generalization mechanism is effective, but that the conditions under which it works must be clearly understood.
Rigorous Hitting Times for Binary Mutations
Authors: Garnier Josselin , Kallel Leila , Schoenauer Marc
Published: February 2008

In the binary evolutionary optimization framework, two mutation operators are theoretically investigated. For both the standard mutation, in which all bits are flipped independently with the same probability, and the 1-bit-flip mutation, which flips exactly one bit per bitstring, the statistical distribution of the first hitting times of the target are thoroughly computed (expectation and variance) up to terms of order l (the size of the bitstrings) in two distinct situations: without any selection, or with the deterministic (1 + l)-ES selection on the OneMax problem. In both cases, the 1-bit-flip mutation convergence time is smaller by a constant (in terms of l) multiplicative factor. These results extend to the case of multiple independent optimizers.
FDA -A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions
Authors: Mühlenbein Heinz , Mahnig Thilo
Published: February 2008

The Factorized Distribution Algorithm (FDA) is an evolutionary algorithm which combines mutation and recombination by using a distribution. The distribution is estimated from a set of selected points. In general, a discrete distribution defined for n binary variables has 2n parameters. Therefore it is too expensive to compute. For additively decomposed discrete functions (ADFs) there exist algorithms which factor the distribution into conditional and marginal distributions. This factorization is used by FDA. The scaling of FDA is investigated theoretically and numerically. The scaling depends on the ADF structure and the specific assignment of function values. Difficult functions on a chain or a tree structure are solved in about O(n√n) operations. More standard genetic algorithms are not able to optimize these functions. FDA is not restricted to exact factorizations. It also works for approximate factorizations as is shown for a circle and a grid structure. By using results from Bayes networks, FDA is extended to LFDA. LFDA computes an approximate factorization using only the data, not the ADF structure. The scaling of LFDA is compared to the scaling of FDA.
On the Scalability of Parallel Genetic Algorithms
Authors: Cantú-Paz Erick , Goldberg David E.
Published: February 2008

This paper examines the scalability of several types of parallel genetic algorithms (GAs). The objective is to determine the optimal number of processors that can be used by each type to minimize the execution time. The first part of the paper considers algorithms with a single population. The investigation focuses on an implementation where the population is distributed to several processors, but the results are applicable to more common masterslave implementations, where the population is entirely stored in a master processor and multiple slaves are used to evaluate the fitness. The second part of the paper deals with parallel GAs with multiple populations. It first considers a bounding case where the connectivity, the migration rate, and the frequency of migrations are set to their maximal values. Then, arbitrary regular topologies with lower migration rates are considered and the frequency of migrations is set to its lowest value. The investigationis mainly theoretical, but experimental evidence with an additively-decomposable function is included to illustrate the accuracy of the theory. In all cases, the calculations show that the optimal number of processors that minimizes the execution time is directly proportional to the square root of the population size and the fitness evaluation time. Since these two factors usually increase as the domain becomes more difficult, the results of the paper suggest that parallel GAs can integrate large numbers of processors and significantly reduce the execution time of many practical applications.
The Recognition of Antonymy by a Language-Enriched Right Hemisphere
Authors: Gazzaniga Michael S. , Miller George A.
Published: February 2008

A commissurotomy patient with limited language in the right hemisphere was tested for ability to recognize direct and indirect antonyms. Normal subjects performing this task recognize direct antonyms faster than indirect. The patient's left hemisphere responded normally, but his right showed no difference in response to direct and indirect antonyms. It is concluded that the patient's right hemisphere had not learned that particular pairs of adjectives (the direct antonyms) can be used to represent the attribute whose contrasting values they express. Such learning normally occurs in the course of reasoning about attributes of things and events, but presumably failed to occur in this patient's right hemisphere because its powers of reasoning are severely limited.