A survey of metaheuristic algorithms for the design of cryptographic Boolean functions

Boolean functions are mathematical objects used in diverse domains and have been actively researched for several decades already. One domain where Boolean functions play an important role is cryptography. There, the plethora of settings one should consider and cryptographic properties that need to be fulfilled makes the search for new Boolean functions still a very active domain. There are several options to construct appropriate Boolean functions: algebraic constructions, random search, and metaheuristics. In this work, we concentrate on metaheuristic approaches and examine the related works appearing in the last 25 years. To the best of our knowledge, this is the first survey work on this topic. Additionally, we provide a new taxonomy of related works and discuss the results obtained. Finally, we finish this survey with potential future research directions.</p

A Survey of Metaheuristic Algorithms for the Design of Cryptographic Boolean Functions

Boolean functions are mathematical objects used in diverse domains and have been actively researched for several decades already. One domain where Boolean functions play an important role is cryptography. There, the plethora of settings one should consider and cryptographic properties that need to be fulfilled makes the search for new Boolean functions still a very active domain. There are several options to construct appropriate Boolean functions: algebraic constructions, random search, and metaheuristics. In this work, we concentrate on metaheuristic approaches and examine the related works appearing in the last 25 years. To the best of our knowledge, this is the first survey work on this topic. Additionally, we provide a new taxonomy of related works and discuss the results obtained. Finally, we finish this survey with potential future research directions

Despacho económico y de unidades en Micro Redes

As a result of the differences between classical large power grids and micro grids a&nbsp;new approach of the Unit Commitment (UC) and Economic Dispatch (ED) problem must be&nbsp;proposed. The high penetrations of renewable sources and distributed energy storage systems, as&nbsp;well as the possibility of working in a grid-connected or island mode are some of the main issues&nbsp;to cope with. Firstly the advantages and drawbacks of the use of the Lambda Iteration Algorithm&nbsp;(LIA) for solving de ED problem in a micro grid are discussed. In order to adapt the LIA to this&nbsp;context some modifications have been carried out. With regard to the Unit Commitment problem,&nbsp;a genetic algorithm with some novel specific operators has been designed. This algorithm is&nbsp;suitable to deal with different constraints and scenarios arising in a micro grid environment. In&nbsp;addition, a comparison between the different characteristics of the designed UC algorithm and&nbsp;the traditional Priority List (PL) method has been performed.Producto de las diferencias existentes entre los sistemas tradicionales de&nbsp;generación y las micro redes (MR), el presente artículo propone un nuevo enfoque en lo que&nbsp;respecta la resolución de los problemas de Despacho Económico (DE) y de Unidades (DU). La&nbsp;fuerte presencia de energías renovables, la incorporación de sistemas de almacenamiento&nbsp;distribuidos y la posibilidad de que la micro red trabaje en isla o interconectada a la red&nbsp;principal son algunos de los aspectos a tener en cuenta a la hora de resolver dichos problemas.&nbsp;Primeramente se analizan las ventajas y desventajas del empleo del Algoritmo de Iteración&nbsp;Lambda (AIL) en la resolución de Despacho Económico, proponiéndose además modificaciones&nbsp;para adaptar el mismo al contexto de las micro redes. En lo que respecta a la resolución del&nbsp;despacho de unidades el artículo propone un algoritmo genético el cual emplea ciertos&nbsp;operadores que facilitan el tratamiento de las restricciones que surgen en este nuevo contexto.&nbsp;Finalmente se lleva a cabo una comparación entre el método de Lista de Prioridades (LP) y el&nbsp;algoritmo genético desarrollado.&nbsp

New evolutionary approaches to protein structure prediction

Programa de doctorado en Biotecnología y Tecnología QuímicaThe problem of Protein Structure Prediction (PSP) is one of the principal topics in Bioinformatics. Multiple approaches have been developed in order to predict the protein structure of a protein. Determining the three dimensional structure of proteins is necessary to understand the functions of molecular protein level. An useful, and commonly used, representation for protein 3D structure is the protein contact map, which represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. This thesis work, includes a compilation of the soft computing techniques for the protein structure prediction problem (secondary and tertiary structures). A novel evolutionary secondary structure predictor is also widely described in this work. Results obtained confirm the validity of our proposal. Furthermore, we also propose a multi-objective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties in order to predict contacts. Results obtained by our approach on four different protein data sets are also presented. Finally, a statistical study was performed to extract valid conclusions from the set of prediction rules generated by our algorithm.Universidad Pablo de Olavide. Centro de Estudios de Postgrad

Ab initio Prediction of the Conformation of Solvated and Adsorbed Proteins

Proteins are among the most important groups of biomolecules, with their biological functions ranging from structural elements to signal transducers between cells. Apart from their biological role, phenomena related to protein behaviour in solutions and at solid interfaces can find a broad range of engineering applications such as in biomedical implants, scaffolds for artificial tissues, bioseparations, biomineralization and biosensors. For both biological and engineering applications, the functionality of a protein is directly related to its three-dimensional structure (i.e. conformation). Methods such as homology and threading that depend on a large database of existing experimental knowledge are the most popular means of predicting the conformation of proteins in their native environment. Lack of sufficient experimentally-derived information for non-native environments such as general solutions and solid interfaces prevents these knowledge-based methods being used for such environments. Resort must, instead, be made to so-called ab initio methods that rely upon knowledge of the primary sequence of the protein, its environment, and the physics of the interatomic interactions. The development of such methods for non-native environments is in its infancy – this thesis reports on the development of such a method and its application to proteins in water and at gas/solid and water/solid interfaces. After introducing the approach used – which is based on evolutionary algorithms (EAs) – we first report a study of polyalanine adsorbed at a gas/solid interface in which a switching behaviour is observed that, to our knowledge, has never been reported before. The next section reports work that shows the combination of the Langevin dipole (LD) solvent method with the Amber potential energy (PE) model is able to yield solvation energies comparable to those of more sophisticated methods at a fraction of the cost, and that the LD method is able to capture effects that arise from inhomogenities in the water structure such as H-bond bridges. The third section reports a study that shows that EA performance and optimal control parameters vary substantially with the PE model. The first three parts form the basis of the last part of the thesis, which reports pioneering work on predicting ab initio the conformation of proteins in solutions and at water/solid interfaces

Optimal configuration, design and operation of batch distillation processes

The overall objective of this thesis is to study the optimal configuration. design and operating policy of batch distillation processes in different separation scenarios. In so doing, this work also aims to provide conceptual insights and compare the performance of the traditional regular column against unconventional columns. In the first part of the thesis, the optimal operation of extractive batch distillation is investigated. A rigorous dynamic optimisation approach based on a detailed model is employed. In addition to the regular column, the optimal operation of the process in the unconventional middle vessel column is examined. The liquid and vapour stream configurations at the middle section of the column is explored for the first time, resulting in improved process performance. The performance of both columns are compared and the results show how their relative performances are affected by different feed compositions. The second part of the thesis is concerned with the simultaneous design and operation of batch distillation processes. The thesis proposes a stochastic optimisation methodology based on genetic algorithm and penalty function. Using the proposed methodology, the simultaneous optimal designs and operations of the regular column for different design scenarios are investigated using rigorous models. Furthermore, the optimal design of the unconventional multivessel column for multicomponent separation is studied for the first time. The effect of different factors such as objective function, feed composition, relative volatility, product specification and number of components on the optimal design of the multivessel system is investigated. A comparison of the performance of the multivessel system with the regular column is also presented. In the final part of the thesis, the feasibility of the genetic algorithm-penalty function approach in tackling simultaneous configuration selection, column sizing and operation is explored. In the case of binary mixture separation, the regular column was found to be more profitable for feeds with a high fraction of the light component whilst the inverted column is optimal for heavier feeds. There exists a flip point, the location of which is case study specific. For the multicomponent separation case study, the multivessel system is found to be superior to both the regular and inverted configuration

Modelling and recognition of protein contact networks by multiple kernel learning and dissimilarity representations

Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces. This equips the system with a two-fold knowledge discovery phase: by analysing the weights, it is possible to check which representations are more suitable for solving the classification problem, whereas the pivotal patterns selected as representatives can give further insights on the modelled system, possibly with the help of field-experts. The proposed classification system is tested on real proteomic data in order to predict proteins' functional role starting from their folded structure: specifically, a set of eight representations are drawn from the graph-based protein folded description. The proposed multiple kernel-based system has also been benchmarked against a clustering-based classification system also able to exploit multiple dissimilarities simultaneously. Computational results show remarkable classification capabilities and the knowledge discovery analysis is in line with current biological knowledge, suggesting the reliability of the proposed system

Competent Program Evolution, Doctoral Dissertation, December 2006

Heuristic optimization methods are adaptive when they sample problem solutions based on knowledge of the search space gathered from past sampling. Recently, competent evolutionary optimization methods have been developed that adapt via probabilistic modeling of the search space. However, their effectiveness requires the existence of a compact problem decomposition in terms of prespecified solution parameters. How can we use these techniques to effectively and reliably solve program learning problems, given that program spaces will rarely have compact decompositions? One method is to manually build a problem-specific representation that is more tractable than the general space. But can this process be automated? My thesis is that the properties of programs and program spaces can be leveraged as inductive bias to reduce the burden of manual representation-building, leading to competent program evolution. The central contributions of this dissertation are a synthesis of the requirements for competent program evolution, and the design of a procedure, meta-optimizing semantic evolutionary search (MOSES), that meets these requirements. In support of my thesis, experimental results are provided to analyze and verify the effectiveness of MOSES, demonstrating scalability and real-world applicability