Evolutionary Approaches to Optimization Problems in Chimera Topologies

Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over $1000$ hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying the characteristics of the Ising instances that influence the success rate of GAs and EDAs.Comment: 8 pages, 5 figures, 3 table

Population Sizing for the Redundant Trivial Voting Mapping

Mapping-Proble

An analysis of the local optima storage capacity of Hopfield network based fitness function models

A Hopfield Neural Network (HNN) with a new weight update rule can be treated as a second order Estimation of Distribution Algorithm (EDA) or Fitness Function Model (FFM) for solving optimisation problems. The HNN models promising solutions and has a capacity for storing a certain number of local optima as low energy attractors. Solutions are generated by sampling the patterns stored in the attractors. The number of attractors a network can store (its capacity) has an impact on solution diversity and, consequently solution quality. This paper introduces two new HNN learning rules and presents the Hopfield EDA (HEDA), which learns weight values from samples of the fitness function. It investigates the attractor storage capacity of the HEDA and shows it to be equal to that known in the literature for a standard HNN. The relationship between HEDA capacity and linkage order is also investigated

DICE: A New Family of Bivariate Estimation of Distribution Algorithms based on Dichotomised Multivariate Gaussian Distributions

A new family of Estimation of Distribution Algorithms (EDAs) for discrete search spaces is presented. The proposed algorithms, which we label DICE (Discrete Correlated Estimation of distribution algorithms) are based, like previous bivariate EDAs such as MIMIC and BMDA, on bivariate marginal distribution models. However, bivariate models previously used in similar discrete EDAs were only able to exploit an O(d) subset of all the O(d2) bivariate variable dependencies between d variables. We introduce, and utilize in DICE, a model based on dichotomised multivariate Gaussian distributions. These models are able to capture and make use of all O(d2) bivariate variable interactions in binary and multary search spaces. This paper tests the performances of these new EDA models and algorithms on a suite of challenging combinatorial optimization problems, and compares their performances to previously used discrete-space bivariate EDA models. EDAs utilizing these new dichotomised Gaussian (DG) models exhibit significantly superior optimization performances, with the performance gap becoming more marked with increasing dimensionality

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms

Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain’s complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks

DICE: Exploiting All Bivariate Dependencies in Binary and Multary Search Spaces

Although some of the earliest Estimation of Distribution Algorithms (EDAs) utilized bivariate marginal distribution models, up to now, all discrete bivariate EDAs had one serious limitation: they were constrained to exploiting only a limited O(d) subset out of all possible O(d2) bivariate dependencies. As a first we present a family of discrete bivariate EDAs that can learn and exploit all O(d2) dependencies between variables, and yet have the same run-time complexity as their more limited counterparts. This family of algorithms, which we label DICE (DIscrete Correlated Estimation of distribution algorithms), is rigorously based on sound statistical principles, and particularly on a modelling technique from statistical physics: dichotomised multivariate Gaussian distributions. Initially (Lane et al. in European Conference on the Applications of Evolutionary Computation, Springer, 1999), DICE was trialled on a suite of combinatorial optimization problems over binary search spaces. Our proposed dichotomised Gaussian (DG) model in DICE significantly outperformed existing discrete bivariate EDAs; crucially, the performance gap increasingly widened as dimensionality of the problems increased. In this comprehensive treatment, we generalise DICE by successfully extending it to multary search spaces that also allow for categorical variables. Because correlation is not wholly meaningful for categorical variables, interactions between such variables cannot be fully modelled by correlation-based approaches such as in the original formulation of DICE. Therefore, here we extend our original DG model to deal with such situations. We test DICE on a challenging test suite of combinatorial optimization problems, which are defined mostly on multary search spaces. While the two versions of DICE outperform each other on different problem instances, they both outperform all the state-of-the-art bivariate EDAs on almost all of the problem instances. This further illustrates that these innovative DICE methods constitute a significant step change in the domain of discrete bivariate EDAs

Neither participation nor revolution: the strategy of the Moroccan Jamiat al-Adl wal-Ihsan

Scholars and students of Islamist movements are divided over the issue of Islamists' commitment to democracy and a number of studies have attempted to discover the true nature of Islamist parties. This paper rejects this approach and argues that the behaviour of Islamist parties can be better understood through an analysis of the constraints and opportunities that their surrounding environment provides. Specifically, the paper aims at explaining the choice of the Moroccan Jamiat al-Adl wal-Ihsan neither to participate in institutional politics nor to undertake violent actions to transform the regime. This is done through an examination of its relations with the other political actors. The paper argues that Jamiat al-Adl wal-Ihsan's behaviour is as much the product of rational thinking as it is of ideology and provides evidence to support this claim. Such findings are important not only in the Moroccan context, but contribute to a growing literature claiming that Islamist movements should be treated as rational political actors operating under 'environmental' constraints and opportunities

Genetic Ancestry-Smoking Interactions and Lung Function in African Americans: A Cohort Study

Background: Smoking tobacco reduces lung function. African Americans have both lower lung function and decreased metabolism of tobacco smoke compared to European Americans. African ancestry is also associated with lower pulmonary function in African Americans. We aimed to determine whether African ancestry modifies the association between smoking and lung function and its rate of decline in African Americans. Methodology/Principal Findings: We evaluated a prospective ongoing cohort of 1,281 African Americans participating in the Health, Aging, and Body Composition (Health ABC) Study initiated in 1997. We also examined an ongoing prospective cohort initiated in 1985 of 1,223 African Americans in the Coronary Artery Disease in Young Adults (CARDIA) Study. Pulmonary function and tobacco smoking exposure were measured at baseline and repeatedly over the follow-up period. Individual genetic ancestry proportions were estimated using ancestry informative markers selected to distinguish European and West African ancestry. African Americans with a high proportion of African ancestry had lower baseline forced expiratory volume in one second (FEV1) per pack-year of smoking (-5.7 ml FEV1/ smoking pack-year) compared with smokers with lower African ancestry (-4.6 ml in FEV1/ smoking pack-year) (interaction P value = 0.17). Longitudinal analyses revealed a suggestive interaction between smoking, and African ancestry on the rate of FEV1 decline in Health ABC and independently replicated in CARDIA. Conclusions/Significance: African American individuals with a high proportion of African ancestry are at greater risk for losing lung function while smoking. © 2012 Aldrich et al