José M. Troya

A survey of parallel distributed genetic algorithms

In this work we review the most important existing developments and future trends in the class of Parallel Genetic Algorithms (PGAs). PGAs are mainly subdivided into coarse and fine grain PGAs, the coarse grain models being the most popular ones. An exceptional characteristic of PGAs is that they are not just the parallel version of a sequential algorithm intended to provide speed gains. Instead, they represent a new kind of meta-heuristics of higher efficiency and efficacy thanks to their structured population and parallel execution. The good robustness of these algorithms on problems of high complexity has led to an increasing number of applications in the fields of artificial intelligence, numeric and combinatorial optimization, business, engineering, etc. We make a formalization of these algorithms, and present a timely and topic survey of their most important traditional and recent technical issues. Besides that, useful summaries on their main applications plus Internet pointers to important web sites are included in order to help new researchers to access this growing area.

Analyzing synchronous and asynchronous parallel distributed genetic algorithms

Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial genetic algorithms (GAs), since they often can be tailored to provide a larger efficiency on complex search problems. In a PGA several sub-algorithms cooperate in parallel to solve the problem. This high-level definition has led to a considerable number of different implementations that preclude direct comparisons and knowledge exchange. To fill this gap we begin by providing a common framework for studying PGAs. We then analyze the importance of the synchronism in the migration step of various parallel distributed GAs. This implementation issue could affect the evaluation effort as well as could provoke some differences in the search time and speedup. We cover in this study a set of popular evolution schemes relating panmictic (steady-state or generational) and structured-population (cellular) GAs for the islands. We aim at extending existing results to structured-population GAs, and also to new problems. The evaluated PGAs demonstrate linear and even super-linear speedup when run in a cluster of workstations. They also show important numerical benefits if compared with their sequential versions. In addition, we always report lower search times for the asynchronous versions.

Compatibility and inheritance in software architectures

The application of formal methods to the development of software depends on the availability of adequate models and formalisms for each of the stages of the development process. In this work, we focus on the level of design called Software Architecture. At this level, the system is described as a collection of interrelated components, and it is here where the properties derived from the system’s structure can be naturally analyzed. Our approach uses process algebras as a formal basis for the description and analysis of software architectures. Process algebras are widely accepted for the speci8cation of software systems. In particular, � -calculus addresses the description of systems with a dynamic or evolving topology, and permits their analysis for bisimilarity and other interesting properties. Though bisimilarity determines the equivalence of behavior, more :exible relations are needed in the context of Software Engineering, in order to support formally the notions of conformance and re8nement of behavior. In this paper we present a relation of compatibility in the context of � -calculus which formalizes the notion of conformance of behavior between software components. Our approach is enhanced with the de8nition of a relation of inheritance among processes. This relation preserves compatibility and indicates whether a process can be considered as a specialization or extension of another one. The suitability of our approach is shown by its application to the 8eld of Software Architecture. c 2001 Elsevier Science B.V. All rights reserved.

Specification and Refinement of Dynamic Software Architectures

Several notations and languages for software architectural specification have been recently proposed. However, some important aspects of composition, extension, and reuse deserve further research. These problems are particularly relevant in the context of open systems, where system structure can evolve dynamically, either by incorporating new components, or by replacing existing components with compatible ones. Our approach tries to address some of these open problems by combining the use of formal methods, particularly process algebras, with concepts coming from the object-oriented domain. In this paper we present LEDA, an Architecture Description Language for the specification, validation, prototyping and construction of dynamic software systems. Systems specified in LEDA can be checked for compatibility, ensuring that the behaviour of their components conforms to each other and that the systems can be safely composed. A notion of polymorphism of behaviour is used to extend and refine components while maintaining their compatibility, allowing the parameterisation of architectures, and encouraging reuse of architectural designs.

Heterogeneous computing and parallel genetic algorithms

This paper analyzes some technical and practical issues concerning the heterogeneous execution of parallel genetic algorithms (PGAs). In order to cope with a plethora of different operating systems, security restrictions, and other problems associated to multi-platform execution, we use Java to implement a distributed PGA model. The distributed PGA runs at the same time on different machines linked by different kinds of communication networks. This algorithm benefits from the computational resources offered by modern LANs and by Internet, therefore allowing researchers to solve more difficult problems by using a large set of available machines. We analyze the way in which such heterogeneous systems affect the genetic search for two problems. Our conclusion is that super-linear performance can be achieved not only in homogeneous but also in heterogeneous clusters of machines. In addition, we study some special features of the running platforms for PGAs, and basically find out that heterogeneous computing can be as efficient or even more efficient than homogeneous computing for parallel heuristics.

Improving flexibility and efficiency by adding parallelism to genetic algorithms

In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivation is to bring some uniformity to the proposal, comparison, and knowledge exchange among the traditionally opposite kinds of serial and parallel GAs. We comparatively analyze the properties of steady-state, generational, and cellular genetic algorithms. Afterwards, this study is extended to consider a distributed model consisting in a ring of GA islands. The analyzed features are the time complexity, selection pressure, schema processing rates, efficacy in finding an optimum, efficiency, speedup, and resistance to scalability. Besides that, we briefly discuss how the migration policy affects the search. Also, some of the search properties of cellular GAs are investigated. The selected benchmark is a representative subset of problems containing real world difficulties. We often conclude that parallel GAs are numerically better and faster than equivalent sequential GAs. Our aim is to shed some light on the advantages and drawbacks of various sequential and parallel GAs to help researchers using them in the very diverse application fields of the evolutionary computation.

A Hybrid Genetic Algorithm for the 0–1 Multiple Knapsack Problem

A hybrid genetic algorithm based in local search is described. Local optimisation is not explicitly performed but it is embedded in the exploration of a search metaspace. This algorithm is applied to a NP-hard problem. When it is compared with other GA-based approaches and an exact technique (a branch and bound algorithm), this algorithm exhibits a better overall performance in both cases. Then, a coarse-grain parallel version is tested, yielding notably improved results.

Adding roles to CORBA objects

Traditional IDLs were defined for describing the services that objects offer, but not those services they require from other objects, nor the relative order in which they expect their methods to be called. Some of the existing proposals try to add protocol information to object interfaces, but most of them fail to do so in a modular way. In this paper we propose an extension of the CORBA IDL that uses a sugared subset of the polyadic /spl pi/-calculus for describing object service protocols, based on the concept of roles. Roles allow the modular specification of the observable behavior of CORBA objects, reducing the complexity of the compatibility tests. Our main aim is the automated checking of protocol interoperability between CORBA objects in open component-based environments, using similar techniques to those used in software architecture description and analysis. In addition, our proposal permits the study of substitutability between CORBA objects, as well as the realization of dynamic compatibility tests during their runtime execution.

Influence of the Migration Policy in Parallel DistributedGAs with Structured and Panmictic Populations

Parallel genetic algorithms (PGAs) have been traditionally used to overcome the intense use of CPU and memory that serial GAs show in complex problems. Non-parallel GAs can be classified into two classes: panmictic and structured-population algorithms. The difference lies in whether any individual in the population can mate with any other one or not. In this work, they are both considered as two reproductive loop types executed in the islands of a parallel distributed GA. Our aim is to extend the existing studies from more conventional sequential islands to other kinds of evolution. A key issue in such a coarse grain PGA is the migration policy, since it governs the exchange of individuals among the islands. This paper investigates the influence of migration frequency and migrant selection in a ring of islands running either steady-state, generational, or cellular GAs. A diversity analysis is also offered from an entropy point of view. The study uses different problem types, namely easy, deceptive, multimodal, NP-Complete, and epistatic search landscapes in order to provide a wide spectrum of problem difficulties to support the results. Large isolation values and random selection of the migrants are demonstrated as providing a larger probability of success and a smaller number of visited points. Also, interesting observations on the relative performance of the different models are offered, as well as we point out the considerable benefits that can accrue from asynchronous migration.

A survey on quality of service support in wireless sensor and actor networks: Requirements and challenges in the context of critical infrastructure protection

Wireless sensor and actor networks (WSANs) are likely to become a pervasive technology in the near future due to the special characteristics of these devices and to the great number of applications where it can be applied. One of these applications is the critical infrastructure protection (CIP). In fact, WSANs have actually been identified as having the potential to become an integral part of the CIP. However, in order to achieve that goal, WSANs need to provide a set of features which includes a robust QoS. Unfortunately QoS support mechanisms in WSANs are still largely undeveloped. This paper studies the state-of-the-art of QoS management in WSANs by exploring existing proposals, challenges and open issues in the field. Emphasis is put on QoS in the context of CIP by focusing on the QoS requirements and the needs of CIP applications. Existing middleware and protocols are surveyed and the challenges and open issues in the field are presented.