Category: Uncategorized

Capturing expert knowledge of mesh refinement in numerical methods of impact analysis by means of genetic programming

Abstract  The mesh refinement decisions of an experienced user of high-velocity impact numerical approximation finite differences computations
are discovered as a set of comprehensible rules by means of Genetic Programming. These rules that …

Abstract  

The mesh refinement decisions of an experienced user of high-velocity impact numerical approximation finite differences computations
are discovered as a set of comprehensible rules by means of Genetic Programming. These rules that could automatically trigger
adaptive mesh refinement to mimic the expert user, detect mesh cells that require refinement by evolving a formula involving
cell quantities such as material densities. Various cell variable combinations are investigated in order to identify the optimal
ones for indicating mesh refinement. A high-velocity impact phenomena example of a tungsten ball that strikes a steel plate
illustrates this methodology.

  • Content Type Journal Article
  • Pages 103-110
  • DOI 10.1007/s00500-010-0684-x
  • Authors
    • Daniel Howard, Howard Science Limited, 24 Sunrise, Malvern, WR14 2NJ UK
    • Adrian Brezulianu, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Iasi, Iasi, Romania

Distributed quantum programming

Abstract  In this paper we explore the structure and applicability of the Distributed Measurement Calculus (DMC), an assembly language
for distributed measurement-based quantum computations. We describe the formal language’s syntax and sem…

Abstract  

In this paper we explore the structure and applicability of the Distributed Measurement Calculus (DMC), an assembly language
for distributed measurement-based quantum computations. We describe the formal language’s syntax and semantics, both operational
and denotational, and state several properties that are crucial to the practical usability of our language, such as equivalence
of our semantics, as well as compositionality and context-freeness of DMC programs. We show how to put these properties to
use by constructing a composite program that implements distributed controlled operations, in the knowledge that the semantics
of this program does not change under the various composition operations. Our formal model is the basis of a quantum virtual
machine construction for distributed quantum computations, which we elaborate upon in the latter part of this work. This virtual
machine embodies the formal semantics of DMC such that programming execution no longer needs to be analysed by hand. Far from
a literal translation, it requires a substantial concretisation of the formal model at the level of data structures, naming
conventions and abstraction mechanisms. At the same time we provide automatisation techniques for program specification where
possible to obtain an expressive and user-friendly programming environment.

  • Content Type Journal Article
  • Pages 1-31
  • DOI 10.1007/s11047-010-9242-9
  • Authors
    • Ellie D’Hondt, Software Languages Lab, Vrije Universiteit Brussel, 10F719, Pleinlaan 2, 1050 Elsene, Belgium
    • Yves Vandriessche, Software Languages Lab, Vrije Universiteit Brussel, 10F719, Pleinlaan 2, 1050 Elsene, Belgium

Quantum value indefiniteness

Abstract  The indeterministic outcome of a measurement of an individual quantum is certified by the impossibility of the simultaneous,
unique, definite, deterministic pre-existence of all conceivable observables from physical conditions of t…

Abstract  

The indeterministic outcome of a measurement of an individual quantum is certified by the impossibility of the simultaneous,
unique, definite, deterministic pre-existence of all conceivable observables from physical conditions of that quantum alone.

  • Content Type Journal Article
  • Pages 1-12
  • DOI 10.1007/s11047-010-9241-x
  • Authors
    • Karl Svozil, Institute for Theoretical Physics, Vienna University of Technology, Wiedner Hauptstraße 8-10/136, 1040 Vienna, Austria

Model accuracy in the Bayesian optimization algorithm

Abstract  Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available.
From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabil…

Abstract  

Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available.
From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of
the population, have brought a new view to evolutionary computation. While solving a given problem with an EDA, the user has
access to a set of models that reveal probabilistic dependencies between variables, an important source of information about
the problem. However, as the complexity of the used models increases, the chance of overfitting and consequently reducing
model interpretability, increases as well. This paper investigates the relationship between the probabilistic models learned
by the Bayesian optimization algorithm (BOA) and the underlying problem structure. The purpose of the paper is threefold.
First, model building in BOA is analyzed to understand how the problem structure is learned. Second, it is shown how the selection
operator can lead to model overfitting in Bayesian EDAs. Third, the scoring metric that guides the search for an adequate
model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection.
Overall, this paper makes a contribution towards understanding and improving model accuracy in BOA, providing more interpretable
models to assist efficiency enhancement techniques and human researchers.

  • Content Type Journal Article
  • Pages 1-21
  • DOI 10.1007/s00500-010-0675-y
  • Authors
    • Claudio F. Lima, Centre for Plant Integrative Biology, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD UK
    • Fernando G. Lobo, Department of Electronics and Informatics Engineering, University of Algarve, Campus de Gambelas, 8000-117 Faro, Portugal
    • Martin Pelikan, Department of Mathematics and Computer Science, 320 CCB, University of Missouri at St. Louis, St. Louis, MO 63121, USA
    • David E. Goldberg, Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

Effect algebras are conditionally residuated structures

Abstract  The aim of the paper was to link up the structures used in foundations of quantum logic and that arising in many-valued reasoning.
It is shown that effect algebras and pseudoeffect algebras can be described as conditionally residua…

Abstract  

The aim of the paper was to link up the structures used in foundations of quantum logic and that arising in many-valued reasoning.
It is shown that effect algebras and pseudoeffect algebras can be described as conditionally residuated structures.

  • Content Type Journal Article
  • Pages 1-5
  • DOI 10.1007/s00500-010-0677-9
  • Authors
    • Ivan Chajda, Department of Algebra and Geometry, Palacký University Olomouc, Tř. 17. listopadu 12, 779 00 Olomouc, Czech Republic
    • Radomír Halaš, Department of Algebra and Geometry, Palacký University Olomouc, Tř. 17. listopadu 12, 779 00 Olomouc, Czech Republic

Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems

Abstract  A bio-inspired artificial immune system is developed to track dynamically the Pareto fronts of time-varying constrained multi-objective
problems with changing variable dimensions. It executes in order T-module, B-module, and M-modu…

Abstract  

A bio-inspired artificial immune system is developed to track dynamically the Pareto fronts of time-varying constrained multi-objective
problems with changing variable dimensions. It executes in order T-module, B-module, and M-module within a run period. The
first module is designed to examine dynamically whether the environment changes or whether a change takes place in the optimization
problem, while creating an initial population by means of the history information. Thereafter, the second one is a loop of
optimization that searches for the desired non-dominated front of a given environment, in which the evolving population is
sorted into several subpopulations. Each of such subpopulations, relying upon the population diversity, suppresses its redundant
individuals and evolves the winners. The last one stores temporarily the resultant non-dominated solutions of the environment
that assist T-module to create some initial candidates helpful for the coming environment. These dynamic characteristics,
along with the comparative experiments guarantee that the artificial immune system can track adaptively the time-varying environment
and maintain the diversity of population while being of potential use for complex dynamic constrained multi-objective problems.

  • Content Type Journal Article
  • Pages 1-17
  • DOI 10.1007/s00500-010-0674-z
  • Authors
    • Zhuhong Zhang, Institute of System Science and Information Technology, College of Science, Guizhou University, Guiyang, Guizhou 550025, China
    • Shuqu Qian, Institute of System Science and Information Technology, College of Science, Guizhou University, Guiyang, Guizhou 550025, China

REGAL-TC: a distributed genetic algorithm for concept learning based on REGAL and the treatment of counterexamples

Abstract  This paper presents a proposal to improve REGAL, a concept learning system based on a distributed genetic algorithm that learns
first-order logic multi-modal concept descriptions in the field of classification tasks. This algorithm…

Abstract  

This paper presents a proposal to improve REGAL, a concept learning system based on a distributed genetic algorithm that learns
first-order logic multi-modal concept descriptions in the field of classification tasks. This algorithm has been a pioneer
system and source of inspiration for others. Studying the philosophy and experimental behaviour of REGAL, we propose some
improvements based principally on a new treatment of counterexamples that promote its underlying goodness in order to achieve
better performances in accuracy, interpretability and scalability, so that the new system meets the main requirements for
classification rules extraction in data mining. The experimental study carried out shows valuable improvements compared with
both REGAL and G-Net distributed genetic algorithms and interesting results compared with some state-of-the-art representative
algorithms in this field.

  • Content Type Journal Article
  • Pages 1-15
  • DOI 10.1007/s00500-010-0678-8
  • Authors
    • L. Ignacio Lopez, Department of Information Technologies, University of Huelva, Palos de la Fra. Huelva, Spain
    • Juan M. Bardallo, Department of Information Technologies, University of Huelva, Palos de la Fra. Huelva, Spain
    • Miguel A. De Vega, Department of Information Technologies, University of Huelva, Palos de la Fra. Huelva, Spain
    • Antonio Peregrin, Department of Information Technologies, University of Huelva, Palos de la Fra. Huelva, Spain

Toward an Estimation of Nadir Objective Vector Using a Hybrid of Evolutionary and Local Search Approaches

A nadir objective vector is constructed from the worst Pareto-optimal objective values in a multiobjective optimization problem and is an important entity to compute because of its significance in estimating the range of objective values in the Pareto-…

A nadir objective vector is constructed from the worst Pareto-optimal objective values in a multiobjective optimization problem and is an important entity to compute because of its significance in estimating the range of objective values in the Pareto-optimal front and also in executing a number of interactive multiobjective optimization techniques. Along with the ideal objective vector, it is also needed for the purpose of normalizing different objectives, so as to facilitate a comparison and agglomeration of the objectives. However, the task of estimating the nadir objective vector necessitates information about the complete Pareto-optimal front and has been reported to be a difficult task, and importantly an unsolved and open research issue. In this paper, we propose certain modifications to an existing evolutionary multiobjective optimization procedure to focus its search toward the extreme objective values and combine it with a reference-point based local search approach to constitute a couple of hybrid procedures for a reliable estimation of the nadir objective vector. With up to 20-objective optimization test problems and on a three-objective engineering design optimization problem, one of the proposed procedures is found to be capable of finding the nadir objective vector reliably. The study clearly shows the significance of an evolutionary computing based search procedure in assisting to solve an age-old important task in the field of multiobjective optimization.

A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments

In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environmen…

In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.