Terence C. Fogarty

Evolving fuzzy rule based controllers using genetic algorithms

Abstract The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh Fuzzy Classifier System # 1 (P-FCS1) is proposed. P-FCS1 is based on the Pittsburgh model of learning classifier systems and employs variable length rule-sets and simultaneously evolves fuzzy set membership functions and relations. A new crossover operator which respects the functional linkage between fuzzy rules with overlapping input fuzzy set membership functions is introduced. Experimental results using P-FCS 1 are reported and compared with other published results. Application of P-FCS1 to a distributed control problem (dynamic routing in computer networks) is also described and experimental results are presented.

Operator and parameter adaptation in genetic algorithms

Abstract Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the “population”. Members of the population are usually represented as strings written over some fixed alphabet, each of which has a scalar value attached to it reflecting its quality or “fitness”. The search may be seen as the iterative application of a number of operators, such as selection, recombination and mutation, to the population with the aim of producing progressively fitter individuals.These operators are usually static, that is to say that their mechanisms, parameters, and probability of application are fixed at the beginning and constant throughout the run of the algorithm. However, there is an increasing body of evidence that not only is there no single choice of operators which is optimal for all problems, but that in fact the optimal choice of operators for a given problem will be time-variant i.e. it will depend on such factors as the degree of convergence of the population. Based on theoretical and practical approaches, a number of authors have proposed methods of adaptively controlling one or more of the operators, usually invoking some kind of “meta-learning” algorithm, in order to try and improve the performance of the Genetic Algorithm as a function optimiser.In this paper we describe the background to these approaches, and suggest a framework for their classification, based on the learning strategy used to control them, and what facets of the algorithm are susceptible to adaptation. We then review a number of significant pieces of work within the context of this setting, and draw some conclusions about the relative merits of various approaches and promising directions for future work.

Information Characteristics and the Structure of Landscapes

Various techniques for statistical analysis of the structure of fitness landscapes have been proposed. An important feature of these techniques is that they study the ruggedness of landscapes by measuring their correlation characteristics. This paper proposes a new information analysis of fitness landscapes. The underlying idea is to consider a fitness landscape as an ensemble of objects that are related to the fitness of neighboring points. Three information characteristics of the ensemble are defined and studied. They are termed: information content, partial information content, and information stability. The information characteristics of a range of landscapes with known correlation features are analyzed in an attempt to reveal the advantages of the information analysis. We show that the proposed analysis is an appropriate tool for investigating the structure of fitness landscapes.

Comparison of steady state and generational genetic algorithms for use in nonstationary environments

The objective of this study is a comparison of two models of the genetic algorithm, the generational and incremental/steady state genetic algorithms, for use in nonstationary/dynamic environments. It is experimentally shown that the choice of a suitable version of the genetic algorithm can improve its performance in such environments. This can extend the ability of the genetic algorithm to track environmental changes which are relatively small and occur with low frequency without the need to implement an additional technique for tracking changing optima.

A Distributed Evolutionary Simulated Annealing Algorithm for Combinatorial Optimisation Problems

In this paper, the Evolutionary Simulated Annealing (ESA) algorithm, its distributed implementation (dESA) and its application to two combinatorial problems are presented. ESA consists of a population, a simulated annealing operator, instead of the more usual reproduction operators used in evolutionary algorithms, and a selection operator. The implementation is based on a multi island (agent) system running on the Distributed Resource Machine (DRM), which is a novel, scalable, distributed virtual machine based on Java technology. As WAN/LAN systems are the most common multi-machine systems, dESA implementation is based on them rather than any other parallel machine. The problems tackled are well-known combinatorial optimisation problems, namely, the classical job-shop scheduling problem and the uncapacitated facility location problem. They are difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit (CPU) time spent. Both applications show that dESA solves problems finding either the optimum or a very near optimum solution within a reasonable time outperforming the recent reported approaches for each one allowing the faster solution of existing problems and the solution of larger problems.

Implementing the Genetic Algorithm on Transputer Based Parallel Processing Systems

The paper discusses the parallel implementation of the genetic algorithm on transputer based parallel processing systems. It considers the implementation of the batch version of the algorithm using a problem from the domain of real-time control. With the problem chosen the evaluation of a member of the population takes a relatively long time, compared with the generation of a member of the population, and so emphasis is laid on parallel evaluation. However, any distribution of processing over a number of processors will involve some communication overheads which are not present when the processing is done on one processor. This overhead will vary depending upon the communication network used. The paper will discuss the trade-offs between communication overheads involved and numbers of processors employed using various communication networks between processors.

Timetabling the Classes of an Entire University with an Evolutionary Algorithm

This paper describes extensions to an evolutionary algorithm that timetables classes for an entire University. A new method of dealing with multi-objectives is described along with a user interface designed for it. New results are given concerning repair of poor recombination choices during local search. New methods are described and evaluated that allow timetables to be produced which have minimal changes compared to a full or partial reference timetable. The paper concludes with a discussion of scale-up issues, and gives some initial results that are very encouraging.

A comparative study of steady state and generational genetic algorithms for use in nonstationary environments

The objective of this study is a comparison of two models of a genetic algorithm — the generational and incremental/steady state genetic algorithms — for use in the nonstationary/dynamic environments. It is experimentally shown that selection of a suitable version of the genetic algorithm can improve performance of the genetic algorithm in such environments.This can extend ability of the genetic algorithm to track the environmental changes which are relatively small and occur with a low frequency without need to implement an additional technique for tracking changing optima.

A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments

In this paper we examine a modification to the genetic algorithm — a new adaptive operator was developed for two industrial applications using genetic algorithm based on-line control systems. The aim is to enable the control systems to track optima of a time-varying dynamic system whilst not being detrimental to its ability to provide sound results for the stationary environments. When compared with the hypermutation operator, the new operator matched the level of diversity introduced into the population with the “degree” of the environmental changes better because it increases population diversity only gradually. Although the new technique was developed for the control application domain where real variables are mostly used, a possible generalization of the method is also suggested. It is believed that the technique has the potential to be a further contribution in making genetic algorithm based techniques more readily usable in industrial control applications.

A simulated annealing algorithm for multi-agent systems: a job-shop scheduling application

In this paper, a parallel implementation of the modular simulated annealing algorithm for classical job-shop scheduling is presented. The implementation is for a multi agent system running on the distributed resource machine, which is a novel, scalable, distributed virtual machine based on Java technology. The problems tackled are well known, difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit time. The empirical results obtained show that the method proposed is successful in comparison with a sequential version of modular simulated annealing algorithm and other methods described in the literature.