Generalized disjunction decomposition for evolvable hardware

Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the “generalized disjunction decomposition” (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using the$(1 + lambda)$evolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided

Open-ended evolution to discover analogue circuits for beyond conventional applications

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Adaptive particle swarm optimization

An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Evolvable Embryonics: 2-in-1 Approach to Self-healing Systems

This paper covers the authors’ recent research in the area of evolutionary design optimisation in electronic application domain (Evolvable Hardware). This will be also presented in the context of biologically inspired systems where Evolvable Hardware is concerned with evolutionary synthesis of self-healing systems and potentially hardware capable of online adaptation to dynamically changing environment. We will also illustrate how EAs can produce novel and unintuitive design solutions, and possibly new design principles. The novelty of this research project addresses this compelling change in the traditional landscape of the associated research disciplines by seeking to provide a novel biologically inspired mechanism to support the design optimisation of self-healing architectures, that is Evolvable-Embryonics

Automatic design of analogue circuits

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Evolvable Hardware (EHW) is a promising area in electronics today. Evolutionary Algorithms (EA), together with a circuit simulation tool or real hardware, automatically designs a circuit for a given problem. The circuits evolved may have unconventional designs and be less dependent on the personal knowledge of a designer. Nowadays, EA are represented by Genetic Algorithms (GA), Genetic Programming (GP) and Evolutionary Strategy (ES). While GA is definitely the most popular tool, GP has rapidly developed in recent years and is notable by its outstanding results. However, to date the use of ES for analogue circuit synthesis has been limited to a few applications. This work is devoted to exploring the potential of ES to create novel analogue designs. The narrative of the thesis starts with a framework of an ES-based system generating simple circuits, such as low pass filters. Then it continues with a step-by-step progression to increasingly sophisticated designs that require additional strength from the system. Finally, it describes the modernization of the system using novel techniques that enable the synthesis of complex multi-pin circuits that are newly evolved. It has been discovered that ES has strong power to synthesize analogue circuits. The circuits evolved in the first part of the thesis exceed similar results made previously using other techniques in a component economy, in the better functioning of the evolved circuits and in the computing power spent to reach the results. The target circuits for evolution in the second half are chosen by the author to challenge the capability of the developed system. By functioning, they do not belong to the conventional analogue domain but to applications that are usually adopted by digital circuits. To solve the design tasks, the system has been gradually developed to support the ability of evolving increasingly complex circuits. As a final result, a state-of-the-art ES-based system has been developed that possesses a novel mutation paradigm, with an ability to create, store and reuse substructures, to adapt the mutation, selection parameters and population size, utilize automatic incremental evolution and use the power of parallel computing. It has been discovered that with the ability to synthesis the most up-to-date multi-pin complex analogue circuits that have ever been automatically synthesized before, the system is capable of synthesizing circuits that are problematic for conventional design with application domains that lay beyond the conventional application domain for analogue circuits