The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system

Use of statistical outlier detection method in adaptive evolutionary algorithms

In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to adaptive methods and soundly outperforms the non-adaptive case

Credit Assignment in Adaptive Evolutionary Algorithms

In this paper, a new method for assigning credit to search\ud operators is presented. Starting with the principle of optimizing\ud search bias, search operators are selected based on an ability to\ud create solutions that are historically linked to future generations.\ud Using a novel framework for defining performance\ud measurements, distributing credit for performance, and the\ud statistical interpretation of this credit, a new adaptive method is\ud developed and shown to outperform a variety of adaptive and\ud non-adaptive competitors

Making and breaking power laws in evolutionary algorithm population dynamics

Deepening our understanding of the characteristics and behaviors of population-based search algorithms remains an important ongoing challenge in Evolutionary Computation. To date however, most studies of Evolutionary Algorithms have only been able to take place within tightly restricted experimental conditions. For instance, many analytical methods can only be applied to canonical algorithmic forms or can only evaluate evolution over simple test functions. Analysis of EA behavior under more complex conditions is needed to broaden our understanding of this population-based search process. This paper presents an approach to analyzing EA behavior that can be applied to a diverse range of algorithm designs and environmental conditions. The approach is based on evaluating an individual’s impact on population dynamics using metrics derived from genealogical graphs.\ud From experiments conducted over a broad range of conditions, some important conclusions are drawn in this study. First, it is determined that very few individuals in an EA population have a significant influence on future population dynamics with the impact size fitting a power law distribution. The power law distribution indicates there is a non-negligible probability that single individuals will dominate the entire population, irrespective of population size. Two EA design features are however found to cause strong changes to this aspect of EA behavior: i) the population topology and ii) the introduction of completely new individuals. If the EA population topology has a long path length or if new (i.e. historically uncoupled) individuals are continually inserted into the population, then power law deviations are observed for large impact sizes. It is concluded that such EA designs can not be dominated by a small number of individuals and hence should theoretically be capable of exhibiting higher degrees of parallel search behavior

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system

Use of Statistical Outlier Detection Method in Adaptive\ud Evolutionary Algorithms

In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to\ud adaptive methods and soundly outperforms the non-adaptive\ud case

Occupational stress and addiction: Possible neurobiological elucidation of medical waste related individuals

Multiple factors contribute to the tendency to develop drug addictions, including social or psychological stressors. Most studies examining causes of and treatments for drug addiction have been conducted in Western developed nations. Here we used phenomenological research approach to explore the neurobiological explanation of drug addiction and to investigate attitudes towards drug use amongst individuals working with medical waste. Data were collected in Dhaka, the capital of Bangladesh, using a variety of techniques based on formal representative sampling for fixed populations and adaptive sampling for roaming populations. We found that over half of participants interviewed reported using illicit substances to cope with occupational stress. Self-reported disease symptoms related to stress were reported by most of the respondents. Working with horrifying waste contributes to increased stress among the participants. These results indicate that in the case of these workers, workplace stressors along with cultural and socio-economic context uniquely contribute to, and exacerbate, tendencies toward drug addiction

Unified theory of phase separation and charge ordering in doped manganite perovskites

A unified theory is developed to explain various types of electronic collective behaviors in doped manganites R$_{1-x}$X$_x$MnO$_3$ (R = La, Pr,Nd etc. and X = Ca, Sr, Ba etc.). Starting from a realistic electronic model, we derive an effective Hamiltonianis by ultilizing the projection perturbation techniques and develop a spin-charge-orbital coherent state theory, in which the Jahn-Teller effect and the orbital degeneracy of e$_g$ electrons in Mn ions are taken into account. Physically, the experimentally observed charge ordering state and electronic phase separation are two macroscopic quantum phenomena with opposite physical mechanisms, and their physical origins are elucidated in this theory. Interplay of the Jahn-Teller effect, the lattice distortion as well as the double exchange mechanism leads to different magnetic structures and to different charge ordering patterns and phase separation.Comment: 10 ReVTEX pages with 4 figures attache

Antiferromagnetic Heisenberg model on anisotropic triangular lattice in the presence of magnetic field

We use Schwinger boson mean field theory to study the antiferromagnetic spin-1/2 Heisenberg model on an anisotropic triangular lattice in the presence of a uniform external magnetic field. We calculate the field dependence of the spin incommensurability in the ordered spin spiral phase, and compare the results to the recent experiments in Cs$_{2}$CuCl$_{4}$ by Coldea et al. (Phys. Rev. Lett. 86, 1335 (2001)).Comment: 4 pages with 4 figures include