First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications

This paper considers the design of reduced complexity two-dimensional FIR filters using genetic algorithms (GAs). Circularly symmetric and diamond shaped low-pass linear phase FIR filters are designed using coefficients comprising the sum or difference of two power-of-two terms. A minimax error criterion is adopted which leads to a minimisation of the weighted ripple extrema in both pass and stop ... View full abstract»

This paper describes a genetic algorithm (GA) for the design of optimal finite word length (FWL) infinite impulse response filters with arbitrary response functions using a cascade of second order sections. Such structures could be used for the implementation of filters for an unrestricted range of responses and are usually characterised by having low sensitivity to variations in word length and s... View full abstract»

The design of digital IIR filters is a multistage process, involving the optimisation of coefficient values, coefficient wordlengths, structure and section ordering. These are traditionally regarded as separate operations, and, as such, can in general only produce filters which are optimal in certain aspects, but not optimal overall. By exploiting the multiple criterion optimisation abilities of t... View full abstract»

This paper develops high performance system identification and linearisation techniques, using a genetic algorithm. The algorithm is fine tuned by simulated annealing, which yields a faster convergence and a more accurate search. This global search technique is used to identify the parameters of a system described by an ARMAX model in the presence of white noise and to approximate a nonlinear mult... View full abstract»

Loudspeaker systems employing more than one driver exhibit off-axis cancellation, which colours the sound heard by the listener. By using knowledge of how the human hearing system works it is possible to hide the errors from the listener, thus giving perfect sound. The design of the filters necessary to achieve the error concealment is a difficult task, and in this paper a method is proposed which... View full abstract»

This paper discusses the application of genetic algorithms (GAs) to the challenging problem of task to processor mapping in the field of real-time parallel processing. Mapping is the off-line allocation of the tasks that represent a parallelised algorithm across a multi-processor architecture. Here, the objective of the optimisation process is to tune the mapping in order to minimise the algorithm... View full abstract»

Kauffman has proposed a theory of cell differentiation using random boolean nets as a model of genetic action. This scheme is used as the basis of a developmental representation in the context of genetic algorithms. A number of simple linear morphologies are evolved using this system View full abstract»

In this paper, we present a technique based on the problem-space genetic algorithm (PSGA) for the static scheduling of directed acyclic graphs onto homogeneous multiprocessor systems to reduce the response-time. The PSGA based approach combines genetic algorithms, with a list scheduling heuristic to search a large solution space efficiently and effectively. Comparison of results with the genetic a... View full abstract»

This paper describes an investigation into using a genetic algorithm to evolve the optimum set of inputs for a neural network. The network is to be used in a novel way for the prediction of nuclear reactor parameters under fault conditions. The development of transients is calculated in a recursive manner. The previous work and the next stage of research are described. The procedure and genetic al... View full abstract»

Genetic algorithms (GAs) have been designed as general purpose optimization methods. GAs can be uniquely characterized by their population-based search strategies and their operators: mutation, selection and crossover. In this paper, we propose a new crossover called multi-step crossover (MSX) which utilizes a neighborhood structure and a distance in the problem space. Given parents, MSX successiv... View full abstract»

A structural genetic algorithm is proposed to optimize the neural network topology and connection weightings. This approach is to partition the genes of chromosome into control genes and connection genes in a hierarchical fashion. The control genes represented in bits are used to govern the layers and neurons activation and considered to be the higher level genes. Whereas the connection genes in t... View full abstract»

This paper aims to illustrate how an existing GA can be modified and set up to explore the relevant trade-offs between multiple objectives with a minimum of effort. While Pareto and Pareto-like ranking schemes can be easily implemented, current guidelines on the associated set-up of techniques such as sharing and mating restriction are intricate and/or based on more or less rough assumptions about... View full abstract»

This paper presents a genetic algorithm for the design of an optimal mesh network. The problem is of relevance in the design of communication networks where the backbone switching network takes the form of a highly connected mesh in order to provide reliability in the event of switch/link failure. The proposed algorithm addresses two important aspects of the problem-topology design and capacity al... View full abstract»

The technique of genetic algorithms is proposed as a means of auto-tuning PID controllers. The technique involves firstly using on-line data and the genetic algorithm to identify a model of the process. Then the identified model, the genetic algorithm and simulation methods, are used to off-line tune the PID controller, so as to minimise a time-domain based cost function. Finally, the genetically ... View full abstract»

The paper presents a novel two-layer learning method for radial basis function (RBF) networks. At the lower layer, a regularised orthogonal least squares (ROLS) algorithm is employed to construct RBF networks while the two key learning parameters, the regularisation parameter and hidden node's width, needed by the ROLS algorithm are optimized using the genetic algorithm at the higher layer. Networ... View full abstract»

Many computer problems today need the computer power that is only available using large scale parallel processing. For a significant number of these problems, the density of the global communications between the individual processors dominates the performance of the whole parallel implementation on a distributed memory multiprocessor system. In these cases, the design of the interconnection networ... View full abstract»

A tree structured genetic algorithm is described. The algorithm is used to generate nonlinear models from process input output data. Three examples are utilised to demonstrate the applicability of the technique within the domain of process engineering View full abstract»

The chosen shape representation defines the subset of the shape space that the Genetic Algorithm can search. If the fitness function can not correctly evaluate all parts of the shape space then we must ensure that the shape representation only defines that subset which can be correctly evaluated. This paper details a parametric description which was used after earlier less restrictive shape repres... View full abstract»

A genetic algorithm (GA) has been compared with a semi-automatic experimental design approach in sequential elimination of levels (SEL). While the GA is a very general method, SEL needs some human input before it can be set up for a particular problem instance-in particular, it needs a human decision on the form of the orthogonal design to be used at each stage. Otherwise, it proceeds in a rather ... View full abstract»

Genetic Algorithm (GA) is a stochastic adaptive algorithm whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. The GA has been adapted to study the problem of designing a stable sliding mode which yields robust performance in variable structure control systems. For various cases, we show that GA is viable and has great potential in the desi... View full abstract»

This paper reviews our recent work in logic optimisation using genetic algorithms (GAs). We have applied GAs to optimisation of combinational and sequential logic, namely: the minimisation of fixed polarity Reed-Muller (RM) expansions, the minimisation of exclusive-OR sum-of-products (ESOP) expansions, and the determination of optimal state assignments for finite or algorithmic state machines (FSM... View full abstract»

The article describes an approach for the self organizing generation of models of complex and unknown processes by means of genetic programming and its application in a biotechnological fed batch production. The approach described combines novel results of computer science-genetic programming-with well known and proven techniques of control and system theory-block diagrams and Z transformation. Th... View full abstract»

The drawbacks of sliding mode control in terms of high control gains and chattering are overcome by incorporating fuzzy control to the switching logic. This hybrid system increases the complexity in design and, at present, there exists no effective design tools due to the lack of analytical and numerical approaches. This paper develops an automated design approach to this design problem, using tou... View full abstract»

A procedure to optimise finite element models of engine structures for low noise using genetic algorithms was investigated. Experiments were performed on a simple engine block model with 1800 degrees of freedom to study the effects of changing the control parameters. The procedure was then applied to the optimisation of a concept level model with 13500 degrees of freedom. Optimisation of this mode... View full abstract»

Many engineering problems may be described as a search for one near optimal description amongst many possibilities, given certain constraints. Search techniques such as genetic programming, seem appropriate to represent many problems. The paper describes a grammatically based learning technique based upon the genetic programming paradigm, that allows declarative biasing and modifies the bias as th... View full abstract»