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S-boxes constitute a cornerstone component in symmetric-key cryptographic algorithms, such as DES and AES encryption systems. In block ciphers, they are typically used to obscure the relationship between the plaintext and the ciphertext. Non-linear and non-correlated S-boxes are the most secure against linear and differential cryptanalysis. In this paper, we focus on a two-fold objective: first, we evolve regular an S-box with high non-linearity and low auto-correlation properties using evolutionary computation; then automatically generate evolvable hardware for the obtained S-box. Targeting the former, we use the Nash equilibrium-based multi-objective evolutionary algorithm to optimise regularity, non-linearity and auto- correlation, which constitute the three main desired properties in resilient S-boxes. Pursuing the latter, we exploit genetic programming to automatically generate the evolvable hardware designs of substitution boxes that minimise hardware space, encryption/decryption time and dissipated power, which form the three main hardware characteristics. We compare our results against existing and well-known designs, which were produced by using conventional methods as well as through evolution. [ABSTRACT FROM AUTHOR]

Protein structure prediction (also known as the protein folding problem) studies the way in which a protein will ‘fold' into its natural state. Due to the enormous complexities involed in accuratly predicting protein structures, many simplifications have been proposed. The Hydrophobic-Hydrophilic (HP) method is one such method of simplifying the problem. In this chapter we introduce a novel method of solving the HP protein folding problem in both two and three dimensions using Ant Colony Optimizations and a distributed programming paradigm. Tests across a small number of processors indicate that the multiple colony distributed ACO (MACO) approach outperforms single colony implementations. Experimental results also demonstrate that the proposed algorithms perform well in terms of network scalability. [ABSTRACT FROM AUTHOR]

Some social systems of natural species, such as flocks of birds and schools of fish, possess interesting collective behavior. In these systems, globally sophisticated behavior emerges from local, indirect communication amongst simple agents with only limited capabilities. In an attempt to simulate this flocking behavior by computers, Kennedy and Eberthart (1995) realized that an optimization problem can be formulated as that of a flock of birds flying across an area seeking a location with abundant food. This observation, together with some abstraction and modification techniques, led to the development of a novel optimization technique - particle swarm optimization. [ABSTRACT FROM AUTHOR]

Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. In this chapter, we propose to implement a parallel EA on consumer-level Graphics Processing Unit (GPU). We perform experiments to compare our parallel EA with an ordinary EA and demonstrate that the former is much more effective than the latter. Since consumer-level graphics processing units are already widely available and installed on oridinary personal computers and they are easy to use and manage, more people will be able to use our parallel algorithm to solve their problems encountered in real-world applications. [ABSTRACT FROM AUTHOR]

In this chapter, we analyze the major traditional parallel models of evolutionary algorithms. The analysis is a contribution in parallel evolutionary computation as unlike previously published studies it is placed within the context of grid computing1. The objective is to visit again the parallel models in order to allow their adaptation to grids taking into account the characteristics of such execution environments in terms of volatility, heterogeneity, large scale and multi-administrative domain. The proposed study is a part of a methodological approach for the development of frameworks dedicated to the reusable design of parallel EAs transparently deployable on computational grids. We give an overview of such frameworks and present a case study related to ParadisEO-CMW which is a porting of ParadisEO onto Condor and MW allowing a transparent deployment of the parallel EAs on grids. [ABSTRACT FROM AUTHOR]

In this work, we present results on analyzing the behavior of a parallel distributed genetic algorithm over different LAN technologies. Our goal is to offer a study on the potential impact in the search mechanics when shifting between LANs. We will address three LANs: a Fast Ethernet network, a Gigabit Ethernet network, and a Myrinet network. We also study the importance of several parameters of the migration policy. The whole analysis will use the DNA fragment assembly problem to show the actual power and utility of the proposed distributed technique. [ABSTRACT FROM AUTHOR]

Reconfigurable Computing is a technique for executing algorithms directly on the hardware in order to accelerate and increase their performance. Recon-figurable hardware consists of programmed FPGA chips for working as specific purpose coprocessors. The algorithms to be executed are programmed by means of description hardware languages and implemented in hardware using synthesis tools. Reconfigurable Computing is very useful for processing high computational cost algorithms because the algorithms implemented in a specific hardware get greater performance than if they are processed by a general purpose conventional processor. So Reconfigurable Computing and parallel techniques have been applied on a genetic algorithm for solving the salesman problem and on a parallel evolutionary algorithm for time series predictions. The hardware implementation of these two problems allows a wide set of tools and techniques to be shown. In both cases satisfactory experimental performances have been obtained. [ABSTRACT FROM AUTHOR]

In this chapter, we propose a massively parallel architecture of a hardware implementation of genetic algorithms. This design is quite innovative as it provides a viable solution to the fitness computation problem, which depends heavily on the problem-specific knowledge. The proposed architecture is completely independent of such specifics. It implements the fitness computation using a neural network. The hardware implementation of the used neural network is stochastic and thus minimises the required hardware area without much increase in response time. Last but not least, we demonstrate the characteristics of the proposed hardware and compare it to existing ones. [ABSTRACT FROM AUTHOR]

Research on multiobjective optimization is very active currently because most of the real-world engineering optimization problems are multiobjective in nature. Multiobjective optimization does not restrict to find a unique single solution, but a set of solutions collectively known as the Pareto front. Evolutionary algorithms (EAs) are especially well-suited for solving such kind of problems because they are able to find multiple trade-off solutions in a single run. However, these algorithms may be computationally expensive because (1) real-world problem optimization typically involves tasks demanding high computational resources and (2) they are aimed at finding the whole front of optimal solutions instead of searching for a single optimum. Parallelizing EAs arises as a possible way of facing this drawback. The first goal of this chapter is to provide the reader with a wide overview of the literature on parallel EAs for multiobjective optimization. Later, we include an experimental study where we develop and analyze pPAES, a parallel EA for multiobjective optimization based on the Pareto Archived Evolution Strategy (PAES). The obtained results show that pPAES is a promising option for solving multiobjective optimization problems. [ABSTRACT FROM AUTHOR]

In this chapter we analyze a model for applying parallel operators in Genetic Algorithms. Here we focus on crossover and varying mutation applied separately in parallel, emphasizing the gains on performance that can be achieved from the concurrent application of operators with different and complementary roles. We analyze the model in single population Genetic Algorithms using deterministic, adaptive, and self-adaptive mutation rate controls and test its performance on a broad range of classes of 0/1 multiple knapsack problems. We compare the proposed model with the conventional one, where varying mutation is also applied after crossover, presenting evidence that varying mutation parallel to crossover gives an efficient framework to achieve higher performance. We also show that the model is superior for online adaptation of parameters and contend that it is a better option for co-adaptation of parameters. In addition, we study the performance of parallel operators within distributed Genetic Algorithms showing that the inclusion of varying mutation parallel to crossover can increase considerably convergence reliability and robustness of the algorithm, reducing substantially communication costs due to migration. [ABSTRACT FROM AUTHOR]

The performance of public-key cryptosystems is primarily determined by the implementation efficiency of the modular multiplication and exponentiation. As the operands, i.e. the plain text of a message or the cipher (possibly a partially ciphered) text are usually large (1024 bits or more), and in order to improve time requirements of the encryption/decryption operations, it is essential to attempt to minimise the number of modular multiplications performed. In this paper, we exploit the ant colony's principles to engineer a minimal addition chain that allows one to compute modular exponentiations very efficiently. The obtained results are compared to existing heuristics as well as to genetically evolved addition chains, i.e. using genetic algorithms. [ABSTRACT FROM AUTHOR]

Smart cards use integrated circuits instead of magnetic tape. Its architecture includes a processor, memory, input/output and, possibly, a cryptographic coprocessor. The cost of smart cards is directly related to the size of the integrated circuit. Our present focus is the cryptographic coprocessor, which usually uses a public-key cryptosystem. In these cryptosystems, the main operation is the modular exponentiation, which is performed using successive modular multiplications. This operation is time consuming for large operands, which is always the case in cryptography. Here, performance is another matter of concern. For fast software or hardware cryptosystems, one needs thus to reduce the total number of modular multiplications required. In this paper, we propose a fast and compact hardware for computing modular exponentiation using the m-ary methods, applying the addition chain method for the pre-processing step. The cryptographic hardware is low-cost and concise, offering a good solution for smart cards. [ABSTRACT FROM AUTHOR]

Modular exponentiation is the main operation to RSA-based public-key cryptosystems. It is performed using successive modular multiplications. This operation is time consuming for large operands, which is always the case in cryptography. For software or hardware fast cryptosystems, one needs thus reducing the total number of modular multiplications required. Existing methods attempt to reduce this number by partitioning the exponent in constant or variable size windows. However, these window-based methods require some pre-computations, which themselves consist of modular exponentiations. It is clear that pre-processing needs to be performed efficiently also. In this paper, we exploit the ant colony strategy to finding an optimal addition sequence that allows one to perform the pre-computations in window-based methods with a minimal number of modular multiplications. Hence we improve the efficiency of modular exponentiation. We compare the yielded addition sequences with those obtained using Brun's algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we focus on engineering Pareto-optimal digital circuits given the expected input/output behaviour with a minimal design effort. The design objectives to be minimised are: hardware area, response time and power consumption. We do so using the Strength Pareto Evolutionary Algorithms. The performance and the quality of the circuit evolved for some benchmarks are presented then compared to those of single objective genetic algorithms as well as to the circuits obtained by human designers. [ABSTRACT FROM AUTHOR]

Pattern matching is essential in many applications such as information retrieval, logic programming, theorem proving, term rewriting and DNA-computing. It usually breaks down into two categories: root and complete pattern matching. Root matching determines whether a subject term is an instance of a pattern in a pattern set while complete matching determines whether a subject term contains a subterm that is an instance of a pattern in a pattern set. For the sake of efficiency, root pattern matching need to be deterministic and lazy. Furthermore, complete pattern matching needs also to be parallel. Unlike root pattern matching, complete matching received little interest from the researchers of the field. In this paper, we present a novel deterministic multi-threaded complete matching method. This method subsumes a deterministic lazy root matching technique that was developped by the authors in previous work. We evaluate the performance of proposed method using theorem-proving and DNA-computing applictions. [ABSTRACT FROM AUTHOR]

The goal of this research is to introduce the challenges associated with modeling nonlinear dynamical systems. Some of these challenges are the nonlinear relationships among system variables, the presence of uncertainty and noise in the measurements. In this research, we have three goals to achieve: (1) provide a review of system identification using Least Square Estimation (LSE); (2) give an introduction to the Takagi-Sugeno (TS) algorithm for developing fuzzy model structure; (3) build a fuzzy model for the Tennessee Eastman (TE) chemical reactor under various operating conditions; The LSE method and the fuzzy method, were used to solve the modeling problem for the TE chemical process reactor. The developed results show that fuzzy models can efficiently perform like the actual chemical reactor and with a high modeling capabilities. [ABSTRACT FROM AUTHOR]

Fuzzy logic is a mathematical tool that can manipulate human reasoning, concepts and linguistic terms. It suits ill-defined systems since it can handle mprecise information about the system model. In designing controllers for mobile robots, it is very difficult to build a model of the robot environment since the robot tasks usually include dynamic and unstructured environments and noisy sensors. [ABSTRACT FROM AUTHOR]

Control systems must ensure in real-world applications good steady-state and dynamic performance. This is the reason why they need high quality servosystems to perform the tasks of stabilization and tracking, and to deal with the problems created by the parameter variance or the nonlinearities. A way to fulfill these tasks is to employ fuzzy control. The development of fuzzy control systems is usually performed by heuristic means, incorporating human skills, but the drawback is in the lack of general-purpose development methods. A major problem, which follows from this way of developing fuzzy controllers, is the analysis of the structural properties of the control system, such as stability, controllability and robustness. This is the reason for the first aim of the chapter, to present stability analysis methods dedicated to fuzzy control systems for servo-systems: the state-space approach, the use of Popov's hyperstability theory, the circle criterion and the harmonic balance method. The second aim of the chapter is to perform the sensitivity analysis of fuzzy control systems with respect to the parametric variations of the controlled plant for a class of servo-systems based on the construction of sensitivity models; both of them, the stability and sensitivity analysis, provide useful information to the development of fuzzy control systems. The presented case studies concerning fuzzy controlled servo-systems validate the presented methods. Since some of the case studies deal with several fuzzy controllers, there are derived useful development conditions for these ones. [ABSTRACT FROM AUTHOR]

In a universe with a single currency, there would be no foreign exchange market, no foreign exchange rates, and no foreign exchange. Over the past twenty-five years, the way the market has performed those tasks has changed enormously. The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. In this chapter, we attempt to compare the performance of hybrid soft computing and hard computing techniques to predict the average monthly forex rates one month ahead. The soft computing models considered are a neural network trained by the scaled conjugate gradient algorithm and a neuro-fuzzy model implementing a multi-output Takagi-Sugeno fuzzy inference system. We also considered Multivariate Adaptive Regression Splines (MARS), Classification and Regression Trees (CART) and a hybrid CART-MARS technique. We considered the exchange rates of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed hybrid models could predict the forex rates more accurately most of the time than all the techniques when applied individually. [ABSTRACT FROM AUTHOR]

This chapter presents an original moving control model based on the fuzzy logic, applied to some navigation special issues. The steps taken on special flight tasks prove adequacy of the fuzzy rules based model to this field. The problems of guiding on interception trajectories (applied to missiles and air fighting), of getting closer and flight coupling (applied to flight refueling or spacecrafts coupling) and of landing are candidate issues in order to be dealt with from the fuzzy perspective. The steps of this chapter are developed from a comparative study perspective involving one of the most popular techniques such as proportional navigation and the proposed fuzzy based method. Three particular forms of the proportional technique are compared against the fuzzy model. One of these particular proportional methods joins the analytic geometric relationship with the fuzzy controlled speed of the interceptor. The fuzzy fully controlled navigation in the proposed model does not impose any analytic relationship between the positions of the aircrafts. The chapter de scribes the methodology followed on fuzzy controller developing. The results emphasize performances of the fuzzy control on simulation cases and the advantages of implementing this type of automatic equipment. [ABSTRACT FROM AUTHOR]

The integration of neural networks and fuzzy inference systems could be formulated into three main categories: cooperative, concurrent and integrated neuro-fuzzy models. We present three different types of cooperative neurofuzzy models namely fuzzy associative memories, fuzzy rule extraction using self-organizing maps and systems capable of learning fuzzy set parameters. Different Mamdani and Takagi-Sugeno type integrated neuro-fuzzy systems are further introduced with a focus on some of the salient features and advantages of the different types of integrated neuro-fuzzy models that have been evolved during the last decade. Some discussions and conclusions are also provided towards the end of the chapter. [ABSTRACT FROM AUTHOR]

In decision making problems, the experts knowledge has different natures: numerical, interval-valued, symbolic, linguistic. . . In other terms, the information is heterogeneous. In this chapter, a qualitative (also symbolic and linguistic) approach for knowledge representation is presented. It is the continuation of studies in a given many-valued logic. First of all, a qualitative approach to manipulate uncertainty is presented as an alternative to classic probabilities. After having defined the linguistic counterpart of the symbolic framework, Generalized Symbolic Modifiers are defined to modify symbolic data usually expressed with linguistic terms. In the context, qualitative data are represented by degrees on a totally ordered scale. As fuzzy modifiers that act on kernels and supports of membership functions, several kinds of symbolic modifiers acting on scales and degrees are proposed. The other object of this work is data combination, especially when data do not have the same importance. The combination operator, called the Symbolic Weighted Median, deals with weights and proposes a representative answer depending on an initial piece of data. One interesting point is that this new median is constructed on the Generalized Symbolic Modifiers. [ABSTRACT FROM AUTHOR]

Computational system modelling is full of ambiguous situations, wherein the designer cannot decide, with precision, what should be the outcome of the system. In [17], L. Zadeh introduced for the first time the concept of fuzziness as opposed to crispiness in data sets. When he invented fuzzy sets together with the underlying theory, Zadeh's main concern was to reduce system complexity and provide designer with a new computing paradigm that allow approximate results. Whenever there is uncertainty, fuzzy logic together with approximate reasoning apply. Fuzzy logic and approximate reasoning [18, 19] can be used in system modelling and control as well as data clustering and prediction [13], to name only few appropriate utilisations. Furthermore, they can be applied to any discipline such as finance [4], image processing [7,16], temperature and pressure control [11, 22], robot control [9, 14], etc. [ABSTRACT FROM AUTHOR]

Pattern matching is a fundamental feature in many applications such as functional programming, logic programming, theorem proving, term rewriting and rule-based expert systems. Usually, patterns are pre-processed into a deterministic finite automaton. Using such an automaton allows one to determine the matched pattern(s) by a single scan of the input term. The matching automaton is typically based on left-to-right traversal of patterns. In this paper, we propose a method to build such an automaton. Then, we propose an incremental method to build a deterministic concise automaton for non-necessarily sequential rewriting systems. With ambiguous patterns a subject term may be an instance of more than one pattern. To select the pattern to use, a priority rule is usually engaged. The pre-processing of the patterns adds new patterns, which are instances of the original ones. When the original patterns are ambiguous, some of the instances supplied may be irrelevant for the matching process. They may cause an unnecessary increase in the space requirements of the automaton and may also reduce the time efficiency of the matching process. Here, we devise a new pre-processing operation that recognises and avoids such irrelevant instances. Hence improves space and time requirements for the matching automaton. [ABSTRACT FROM AUTHOR]