Biomedical subjects

David E Goldberg

Publications and source records attributed to David E Goldberg.

10 recordsLinked to original sources

PubMed2006

Automated global structure extraction for effective local building block processing in XCS.

Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently. In such problems, standard variation operators of genetic and evolutionary algorithms used in LCSs suffer from potential disruption of groups of interacting features, resulting in poor performance. This paper introduces efficient crossover operators to XCS by incorporating techniques derived from competent GAs: the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of simple crossover operators such as uniform crossover or one-point crossover, ECGA or BOA-derived mechanisms are used to build a probabilistic model of the global population and to generate offspring classifiers locally using the model. Several offspring generation variations are introduced and evaluated. The results show that it is possible to achieve performance similar to runs with an informed crossover operator that is specifically designed to yield ideal problem-dependent exploration, exploiting provided problem structure information. Thus, we create the first competent LCSs, XCS/ECGA and XCS/BOA, that detect dependency structures online and propagate corresponding lower-level dependency structures effectively without any information about these structures given in advance.

Algorithms↗

PubMed2005

Efficient genetic algorithms using discretization scheduling.

In many applications of genetic algorithms, there is a tradeoff between speed and accuracy in fitness evaluations when evaluations use numerical methods with varying discretization. In these types of applications, the cost and accuracy vary from discretization errors when implicit or explicit quadrature is used to estimate the function evaluations. This paper examines discretization scheduling, or how to vary the discretization within the genetic algorithm in order to use the least amount of computation time for a solution of a desired quality. The effectiveness of discretization scheduling can be determined by comparing its computation time to the computation time of a GA using a constant discretization. There are three ingredients for the discretization scheduling: population sizing, estimated time for each function evaluation and predicted convergence time analysis. Idealized one- and two-dimensional experiments and an inverse groundwater application illustrate the computational savings to be achieved from using discretization scheduling.

Algorithms↗

PubMed2005

Convergence time for the linkage learning genetic algorithm.

This paper identifies the sequential behavior of the linkage learning genetic algorithm, introduces the tightness time model for a single building block, and develops the connection between the sequential behavior and the tightness time model. By integrating the first-building-block model based on the sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by the linkage learning genetic algorithm when solving uniformly scaled problems.

Algorithms↗

PubMed2003

Analysis and improvement of fitness exploitation in XCS: bounding models, tournament selection, and bilateral accuracy.

The evolutionary learning mechanism in XCS strongly depends on its accuracy-based fitness approach. The approach is meant to result in an evolutionary drive from classifiers of low accuracy to those of high accuracy. Since, given inaccuracy, lower specificity often corresponds to lower accuracy, fitness pressure most often also results in a pressure towards higher specificity. Moreover, fitness pressure should cause the evolutionary process to be innovative in that it combines low-order building blocks of lower accurate classifiers, to higher-order building blocks with higher accuracy. This paper investigates how, when, and where accuracy-based fitness results in successful rule evolution in XCS. Along the way, a weakness in the current proportionate selection method in XCS is identified. Several problem bounds are derived that need to be obeyed to enable proper evolutionary pressure. Moreover, a fitness dilemma is identified that causes accuracy-based fitness to be misleading. Improvements are introduced to XCS to make fitness pressure more robust and overcome the fitness dilemma. Specifically, (1) tournament selection results in a much better fitness-bias exploitation, and (2) bilateral accuracy prevents the fitness dilemma. While the improvements stand for themselves, we believe they also contribute to the ultimate goal of an evolutionary learning system that is able to solve decomposable machine-learning problems quickly, accurately,and reliably. The paper also contributes to the further understanding of XCS in general and the fitness approach in XCS in particular.

Algorithms↗

PubMed2003

Bounding the effect of noise in multiobjective learning classifier systems.

This paper analyzes the impact of using noisy data sets in Pittsburgh-style learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with noisy domains. For this reason, we developed a theoretical model for predicting the minimal achievable error in noisy domains. Combining this theoretical model for crisp learners with graphical representations of the evolved hypotheses through multiobjective techniques, we are able to bound the behavior of a learning classifier system. This kind of modeling lets us identify relevant characteristics of the evolved hypotheses, such as overfitting conditions that lead to hypotheses that poorly generalize the concept to be learned.

Algorithms↗

PubMed2003

Redundant representations in evolutionary computation.

This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redundant representations. Representations are synonymously redundant if the genotypes that represent the same phenotype are very similar to each other. Non-synonymously redundant representations do not allow genetic operators to work properly and result in a lower performance of evolutionary search. When using synonymously redundant representations, the performance of selectorecombinative genetic algorithms (GAs) depends on the modification of the initial supply. We have developed theoretical models for synonymously redundant representations that show the necessary population size to solve a problem and the number of generations goes with O(2(kr)/r), where kr is the order of redundancy and r is the number of genotypic building blocks (BB) that represent the optimal phenotypic BB. As a result, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, non-uniformly redundant representations can only be used advantageously if a-priori information exists regarding the optimal solution. The validity of the proposed theoretical concepts is illustrated for the binary trivial voting mapping and the real-valued link-biased encoding. Our empirical investigations show that the developed population sizing and time to convergence models allow an accurate prediction of the empirical results.

Algorithms↗

PubMed2002

Inosine induces axonal rewiring and improves behavioral outcome after stroke.

Cerebral infarct (stroke) often causes devastating and irreversible losses of function, in part because of the brain's limited capacity for anatomical reorganization. The purine nucleoside inosine has previously been shown to induce neurons to express a set of growth-associated proteins and to extend axons in culture and in vivo. We show here that in adult rats with unilateral cortical infarcts, inosine stimulated neurons on the undamaged side of the brain to extend new projections to denervated areas of the midbrain and spinal cord. This growth was paralleled by improved performance on several behavioral measures.

Animals↗

PubMed2002

Inosine stimulates axon growth in vitro and in the adult CNS.

Unlike mammals, lower vertebrates can regenerate their optic nerves and certain other CNS pathways throughout life. To identify the molecular bases of this phenomenon, we developed a cell culture model and found that goldfish retinal ganglion cells will regenerate their axons in response to the purine nucleoside inosine. Inosine acts through a direct intracellular mechanism and induces many of the changes in gene expression that underlie regenerative growth in vivo, e.g., upregulation of GAP-43, T alpha-1 tubulin, and the cell-adhesion molecule, L1. N-kinase, a 47-49-kDa serine-threonine kinase, may mediate the effects of inosine and serve as part of the modular signal transduction pathway that controls axon growth. In vivo, inosine stimulates extensive axon growth in the mature rat corticospinal tract. Following unilateral transection of the corticospinal tract, inosine applied to the intact sensorimotor cortex stimulated layer 5 pyramidal cells to upregulate GAP-43 expression and to sprout axon collaterals. These collaterals crossed the midline at the level of the cervical enlargement and reinnervated regions whose normal connections had been served. Further understanding of the molecular changes that lie upstream and downstream of N-kinase may lead to new insights into the control of axon growth and to novel methods to improve functional outcome in patients with CNS injury.

Animals↗

PubMed2002

Network random keys - a tree representation scheme for genetic and evolutionary algorithms.

When using genetic and evolutionary algorithms for network design, choosing a good representation scheme for the construction of the genotype is important for algorithm performance. One of the most common representation schemes for networks is the characteristic vector representation. However, with encoding trees, and using crossover and mutation, invalid individuals occur that are either under- or over-specified. When constructing the offspring or repairing the invalid individuals that do not represent a tree, it is impossible to distinguish between the importance of the links that should be used. These problems can be overcome by transferring the concept of random keys from scheduling and ordering problems to the encoding of trees. This paper investigates the performance of a simple genetic algorithm (SGA) using network random keys (NetKeys) for the one-max tree and a real-world problem. The comparison between the network random keys and the characteristic vector encoding shows that despite the effects of stealth mutation, which favors the characteristic vector representation, selectorecombinative SGAs with NetKeys have some advantages for small and easy optimization problems. With more complex problems, SGAs with network random keys significantly outperform SGAs using characteristic vectors. This paper shows that random keys can be used for the encoding of trees, and that genetic algorithms using network random keys are able to solve complex tree problems much faster than when using the characteristic vector. Users should therefore be encouraged to use network random keys for the representation of trees.

Algorithms↗

PubMed2002

Spin-flip symmetry and synchronization.

In the context of optimization by evolutionary algorithms (EAs), epistasis, deception, and scaling are well-known examples of problem difficulty characteristics. The presence of one such characteristic in the representation of a search problem indicates a certain type of difficulty the EA is to encounter during its search for globally optimal configurations. In this paper, we claim that the occurrence of symmetry in the representation is another problem difficulty characteristic and discuss one particular form, spin-flip symmetry, characterized by fitness invariant permutations on the alphabet. Its usual effect on unspecialized EAs, premature convergence due to synchronization problems, is discussed in detail. We discuss five different ways to specialize EAs to cope with the symmetry: adapting the genetic operators, changing the fitness function, using a niching technique, using a distributed EA, and attaching a highly redundant genotype-phenotype mapping.

Algorithms↗